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BUGSSGF 2011 Papers - Index
375 papers. Source: SAS Global Forum conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %DO Loop - A Simple Dynamic Programming Technique | Yunchao (Susan) Tian | Dynamic programming is an advanced macro topic. This paper presents a simple dynamic programming techniq ue, the %DO loop. Included topics are: 1. Generating a list of variables, such as VAR1A - VAR50A, VAR1B - VAR50B. 2. Label ing a series of variables. 3. Run ning the same procedure against a series of data sets. 4. Using %IF- %THEN statement within %DO loop to provide logical branches. Each of the topics will be discussed along with examples. This paper is intended for SAS users with basic knowledge of macro language | do loop, macro loop, label statement, %if-%then-%else | source | 3 |
| 2 | %GetTweet: A New SAS Macro to Fetch and Summarize Tweets | Satish Garla, Goutam Chakraborty |
The role of Twitter as a source of valuable information for spotting trends has been much talked about in the popular press. The open API in Twitter makes it one of the most sought after platforms for textual data analysis. While SAS Text Miner provides a robust method for analyzing textual data, the challenge remains to fetch customized Tweets and clean textual data before any text mining. This paper develops and discusses a new SAS macro that can be used easily to fetch the Tweets a researcher wants from Twitter. The macro uses the search API in Twitter and the HTTP procedure in SAS to create a data set of Tweets that are customized using parameters such as combination of keywords, exact phrases, omission of specific words, and so on. The macro also purges terms such as http tags and URLs that might create problems in text mining. This paper also shows a visual analysis of "retweets" to identify influencers via network visualization graphs that are available in SAS/GRAPH software |
proc step, create table, proc http, dataset, group by, keyword parameter, %macro parameters, macro variable, proc freq, proc sql | source | 12 |
| 3 | A Better Characterization Routine | Patricia Hettinger | One of the great things about SAS Enterprise Guide is the ability to get usable results without a lot of coding. Another great thing is the ability to take the code generated by an Enterprise Guide task and improve upon it. The standard data characterization task in SAS Enterprise Guide gives some important information about your data but has several drawbacks. One is that it will run frequencies on all character data regardless of length or number of distinct values. This can result in some variables being dropped from the output due to too many distinct values. It also results in frequencies not being run for numeric values at all. Another is that minimum, maximum, number of missing values and number of non-missing values will be calculated only for numeric variables when this information would be useful for any variable. A third issue is the likelihood of your system hanging when attempting to analyze large data sets with many variables | proc freq, call symput, proc step, proc sql | source | 11 |
| 4 | A Better Way to Search Text: Perl Regular Expressions in SAS | Brad Lagle, Kevin McGowan |
Many programmers use regular expressions to make their programs more efficient, especially when the program processes large amounts of text format data. One popular type of regular expressions are Perl regular expressions. Starting with version 8 of SAS, the power of Perl regular expressions is now available to SAS programmers. This paper provides a short introduction to Perl regular expressions in SAS. The reader should already have a basic knowledge of SAS DATA STEP programming and some knowledge about how to create regular expressions |
sas programmer, data step | source | 7 |
| 5 | A Detailed Examination of Workload Assigned and Faculty Productivity: Social Networks Using SAS Enterprise Guide and SAS Text Miner | Yehia H. Khalil | Several motivations drive the need for truthful faculty workload and productivity analysis, including enhancing educational quality and resource reduction under the current economic crisis. The purpose of this study is to examine assigned faculty workloads in Biology and Mathematics D epartments and to compare them with faculty productivity as listed in faculty workload assignments. Some departm ents tend to use minimum administrative positions while others use higher numbers. This study us ed publicly available records at the University of Louisville to compare and contrast the two departments. Enterprise Guide and SAS Text Miner were employed to investigate the data within the context of social network analysis. It was shown that Biology is more productive compared to Mathematics in terms of research and grants received. Also, it was shown that such global comparisons between departments are useful for faculty outcomes optimization | social network analysis, proc mds | source | 21 |
| 6 | A Goal Programming Approach to Strategic Bank Balance Sheet Management | Machiel F. Kruger | Efficiently managing a bank's balance sheet while maximizi ng returns and at the same time taking into account conflicting goals such as minimizing risk, subject to regulato ry and managerial constraints, is a complex task. Using a trial and error approach can only deliver sub-optimal solutions. Given the cost of capital, the total capital available and bank management's risk appetite, managers need to answer the question of whether there exists an 'optimal' balance sheet composition of assets and liabilities that will enable their organization to achieve its strategic goals. Taking the current balance sheet as a starting point, this paper proposes a multi-objective approach to move from the current balance sheet to the 'optimal' balance sheet, whilst taking Basel Pillars 1 and 2 regulatory capital limits into account | expected loss, basel iii, capital adequacy, odds ratio, proc step | source | 11 |
| 7 | A Macro for Safely Merging Many Data Sets | Taylor R. Young | When SAS merges datasets that have variable names in common, only one instance of the variable name is preserved and the last occurrence of a shared variable name overwrites all previously encountered data. As a result, a SAS user working with unfamil iar datasets may unknowingly lo se valuable data particularly if the merge involves a large number of datasets and/or variables. Expanding on previously publishe d methods using PROC SQL and the DICTIONARY.COLUMNS table to dynamically rename common variables before merging two datasets, this paper will present methodology to safely merge all datasets in a library by dynamically renaming and relabeling any variables with the same name |
do loop, macro variable, proc sql, proc step, label statement | source | 5 |
| 8 | A Macro to Modify Attributes for All Character Variables in SAS Data Sets | Suresh B. Kadaru | Often SAS programmers modify the attributes of text columns in data tables as part of their SAS routines. Some of these modifications might be necessary to avoid erroneous output results, especially while performing SQL queries, DATA STEP merges, or while subsetting tables. In such cases, a macro can be used to apply desired SAS character functions across all variables in incoming data tables. Thus, there is no need to repeatedly type the function names in SAS statements, eliminating the risk of forgetting the use of necessary functions in the code, and providing flexibility to incorporate the latest SAS functions. This macro has a high utility value when it is run in a production environment ahead of other user tasks, without altering the existing code. Currently, this macro can handle several of the SAS character functions that modify the attributes of text variables and do not return a value after execution |
sas function, character variable, dataset, sas log, character functions, macro variable, proc sql, error message, proc step, proc contents, sample, macro loop, update statement | source | 6 |
| 9 | A Match Made in Heaven: Predictive Analytics and the SAS BI Dashboard 4.3 | Keith Renison | The use of dashboards to provide critical insight into the past is becoming a standard across virtually all organizations. This importance is made evident by the fantastic evolution of the SAS BI Dashboard, specifically with the release of SAS BI Dashboard 4.3. How then, are analytics integrated into the strategic views of the organization? That is, what role does predictive modeling play in the use of dashboards, and in effective performance measurement? This paper explores the connection and practical application of predictive analytics in conjunction with data visualizations in SAS BI Dashboard 4.3 to not just view the past, but propel your dashboards into the future | predictor variable, correlation, date variable, proc step | source | 13 |
| 10 | A Non-Invasive Macro to Track Submission Metadata in SAS Drug Development | Katrina E. Canonizado, Bradford J. Danner, Matthew J. Wiedel |
To address the concerns of program quality and transparency in the contract research organization (CRO) industry, program validation plays a central role in data analysis. As an aid to speed up this process, we provide fellow programmers and reviewers with what we refer as an au dit program list. This list contains the current settings of the SAS session like the program name, data sets used, etc. In SAS Drug Development, this information is stored in SDDparms for individual programs within submission. This paper demonstrates bri efly the macro %metadata we have written. The macro primarily utilizes SDDparms in tracking the information to build the audit program list | proc sql, macro variable, proc step, dataset, clinical research, permanent dataset, csr, clinical trial, order by, %if-%then-%else | source | 8 |
| 11 | A Practical Approach to Re-Architecting a SAS Deployment | Jim Fenton, Robert Ladd, Gary Spakes |
In today's business environment, enterprise computing deployments must be able to handle the challenges that companies face while adhering to IT standards. With the SAS platform being a multi-tiered environment consisting of components residing on client desktops, middle-tier Web servers, compute servers, and data assets, SAS customers are looking for ways to modernize their environment without breaking the bank. When looking to modernize your SAS environment, there are many things to consider. This paper takes a practical approach to re-architecting SAS environments. It identifies typical architecture scenarios while taking into consideration that each customer has unique needs. This paper can be used as a reference when looking to modernize your SAS environment | sas grid, sas analyst | source | 9 |
| 12 | A SAS Macro to Consolidate Data from Multiple Cards with Varying Time Validity but Belonging to a Single Account | Sohail Mohammad, Goutam Chakraborty |
Many business-to-business (B2B) marketers use loyalty CARDS to recognize their loyal customers and give them benefits for their continued patronage. A B2B customer typically has a single corporate account with the seller. But, the customer often wants multiple loyalty CARDS that it can distribute to its employees who will be using the seller's product and services for earning loyalty points. The loyalty CARDS points are often accrued over varying time frames and sometimes CARDS become inactive or lost and need to be replaced. Problems arise in consolidating loyalty points in situations where CARDS are anonymous and lost or inactive CARDS are simply replaced by new ones. We develop and report a SAS Macro to solve this problem |
txt, missing value | source | 6 |
| 13 | A SAS Macro to Validate Business Rule for Phasing Derivation in a Clinical Data Repository | Ellen Xiao | This paper describes the business rules for phasing derivations and the related concepts in Merck's Clinical Data Repository (CDR). It introduces the %CHKPHASERULE macro, a tool to validate the phasing derivations for all related domains in the CDR. This macro was created utilizing SAS/MACRO with version 9.1.3. The business rules for phasing derivations includes two parts (ruleXXA and ruleXXB). By using the Metadata Setup specifications, the programmers can easily identify which domains relate to which part of this business rule to ensure the macro is being called correctly. This tool can test to see if the phasing derivations are following the tie-breaker rules defined in Metadata setup for different domains. This reduces many tedious jobs to check epochs derived from domain to domain manually in the CDR. It offers benefits and process improvement to the business rules validation programmers |
data validation rules, clinical data | source | 6 |
| 14 | A SAS Programmer's Guide to Stored Processes | Joe M. Flynn | If you have SAS programming knowledge and experience, then fully leveraging stored processes in your organization can be a simple task. This paper explains how to develop a stored process by using SAS Enterprise Guide 4.3. Topics include: defining and registering metadata-permissions; responsibilities; SAS folders; keywords writing the SAS code-Output Delivery System (ODS) output; stored process macros; prompts; reporting options selecting execution options-STP server; workspace server; result types; source code repositories; operating system access defining prompts-the prompt model; output parameters * determining data sources and targets-input streams; output streams This paper also includes practical examples that are based on questions submitted to SAS Technical Support |
sas enterprise guide, macro variable, excel, date variable, sas programmer, proc sql, where statement, proc step | source | 17 |
| 15 | A Side of Hash for You to Dig Into | Shan A. Rasul | Within the realm of Customer Relationship Management (CRM) there is always a need for segmenting customers in order to gain insights into customer behaviour and being able to use that information to better align customers with current and ongoing marketing strategies. This paper discusses a macro created using SAS version 9.2, which makes use of the DATA STEP hash object for the purposes of customer segmentation |
hash table, do loop, customer segmentation, transition matrix, proc freq | source | 7 |
| 16 | Absolutely Fabulous: Tips on Creating a Publication-Ready Report Using ODS Absolute Layout Functionality | Gina Huff, Tuesdi Helbig, Chris James |
Are you using SAS but formatting in EXCEL or Word? Are you creating SAS output just to turn right around and re- enter the data into publishing software (i.e. Microsoft Publisher, Adobe InDesi gn) to create a report that is "publication ready"? If you're like me, you dreamed of the day that you could create SAS Output that was Absolutely Fabulous; output that could take you straight from SAS program to finished report. Dream no more, ODS LAYOU T is here! Although still pre-production in version 9.2, I have found ODS LAYOUT to be a powerful, user -friendly way of putting multiple data elements on the page. Couple that with the styling capabilities of SAS procedures and other ODS statements and you are well on your way to creating your own Absolutely Fabulous Report. This paper will take you on a tour of the tools I used, along with some helpful tips, words of caution, and valuable resources |
output delivery system, proc step, macro variable, proc format, ods pdf, proc report, call symput, sas base, sas output, proc tabulate, sas enterprise guide, proc gplot, proc template, proc sql | source | 14 |
| 17 | Achieving Efficiencies Using SAS Drug Development | Aik Hoe Seah | More pharmaceutical and biotechnology companies are using SAS Drug Development to handle clinical data and perform statistical analyses. As a r esult, many users who are transitioning from Base SAS find out that they have to change many habits to use SAS Drug Development effectively. This presentation discusses some pros and cons of working with SAS Drug Development. It also provides some alterna tive models in SAS Drug Development that might improve performance and efficiency. Keywords: SDD, SAS Drug Development, WebDAV, Job Editor, Scheduler, Process Editor | sas base, clinical research, macro parameter, proc export, infile statement | source | 12 |
| 18 | Add Style to ODS Output by Stretching Your Inheritance in SAS 9.2 | Perry Watts | Inheritance in ODS style templates has been simplified in Version 9.2 SAS. Now you don't need a FROM clause in a CLASS statement when you work with defined lineages in the Styles.Default template. However, lineages as well as attributes can be changed when new style templates are created in ODS. This means that you need an ODS lineage tracer that links inheritance from Base.Template.Style to associated attribute settings in the Styles.Default template. Only by viewing attribute settings at their assigned locations in a lineage, is it possible to know for certain when STYLE … FROM can safely be discarded in favor of the CLASS statement. This paper shows by example how to use the lineage tracer plus additional tools to develop new styles 1 |
proc step, proc freq, proc template, proc report, output delivery system, proc print, format statement, ods html, proc sql | source | 21 |
| 19 | After SAS Performs the Surgery, Excel Applies the Make-Up: Making Healthcare Data Look Pretty | Andrea Scott | Sometimes we are asked to perform what is seemingly impossible: turn Healthcare data int o a simple and understandable message. How is it possible to make Medical or Pharmacy claims, or enrollment data look pretty? What better way to do this than by breaking the data down to its simplest form (not necessarily a simple task) then transforming the numbers into colors. Shading can be used to denote intensity of dosage or addition of claims plus enrollment; contrasts can be used to compare populations. There are times when words hinder the message. The visuals speak for themselves. Numbers aren't always the optimal way to communicate analytic results | excel, proc step, insurance claims, health insurance, binary variable, proc transpose, insurer | source | 5 |
| 20 | An Application of SAS Simulation Studio: The Microsimulation of a Computer-Assisted Telephone Interviewing System | Yves Belanger, Kristen Couture, Elisabeth Neusy |
In order to carry on large -scale telephone surveys, statistical agencies commonly use Computer Assisted Telephone Interviewing (CATI). For example, Blaise is the CATI system used at Statistics Canada. One of the featu res of Blaise is a call scheduler that regulates how interviews are scheduled and conducted, depending on a variety of parameters. It is not easy to find optimal values for these parameters, as field tests in real time are typically costly and take time to conduct and analyze. It is important, however, to try to find strategies that decrease the costs of interviewing , given that data collection usually accounts for the largest part of a survey's overall budget. In this paper we show how microsimulation ca n be used to help find strategies to improve the efficiency of data collection. More specifically, w e describe the characteristics of a prototype of a microsimulation system for CATI surveys built using SAS Simulation Studio | sample, predictor variable, dataset, logistic distribution, logistic regression, regression analysis | source | 8 |
| 21 | An Insurance company implementing Personal Loan marketing campaigns using SAS Marketing Automation | Young Chun Kim | SAS Marketing Automation provides a variety of functions which is needed for personal loan marketing campaign process in the insurance industry. This paper, illustrated from the insurance industry, describes a case study for SAS Marketing Automation which was implemented to perform personal loan marketing activities effectively and efficiently, - identify campaign targets among the existing insurance policy holders. For example, who is in more need of a personal loan? Who is seeking additional credit? What characteristic of customers is associated with loan potential? How do you design a marketing data mart that can accommodate the above issues? In this case, the insurance company implemented a personal loan marketing campaign system through the components and functions provided by SAS Marketing Automation. Prior to SAS Marketing Automation, marketers and analysts dedicated most of their time performing their daily routines manually, but post implementation, the system would execute routine tasks automatically and allow the business users to focus on making strategic decisions and developing marketing strategies. Business users' routine tasks would include, downloading insurance policy holders and customer contact data into their desktop from Enterprise Data Warehouse (EDW) for target list generation, then applying campaign selection rule and exclusion rule, which typically required days of work - Needless to say, the new marketing system now requires only couple of hours to perform similar task. Generating reports for campaign performance analysis also consumed bulk of time as it involved several steps of manual task. However implementation of SAS Marketing Automation also allowed this manual task to be simplified, only requiring couple of clicks from the business user. The underneath data source which is used to generate these reports such as insurance policy information, customer contact history information, loan history information and campaign history information, etc are merged and uploaded to the marketing mart automatically. | consumer loan, policy, insurance | source | 5 |
| 22 | An Introduction to Survival Analysis Using Complex Sample Survey Data | Patricia A. Berglund | This paper presents practical guidance on conducting survival analysis using data derived from a complex sample survey. Survival curves, Cox models, and discrete-time logistic regression are demonstrated through use of PROC LIFETEST, PROC SGPLOT, PROC SURVEYPHREG and PROC SURVEYLOGISTIC. The analytic techniques presented can be used on any operating system and are intended for an intermediate level audience |
survival analysis, proc step, sample, proc phreg, proc surveylogistic, proportional hazards assumption, degrees of freedom, hazard ratio, null hypothesis, logistic regression, logistic distribution, proc sgplot, regression analysis, response variable, data science, hazard function, right censoring | source | 20 |
| 23 | Annotated Plot of z/OS Auxiliary Storage Local Page Slot Allocation Using SAS/GRAPH and MXG Software | Neal Musitano Jr. | This paper is about creating an annotated SAS/GRAPH plot of z/OS local page dataset slot utilization. The z/OS local page dataset sl ots are used by z/OS for management of system memory. The annotated plot shows the count of the local page slots defined and the number of slots that are used. A red star highlights the maximum local slot usage. Through the day, s lots are used and released with copies of main memory information. For z/OS performance reasons the MXG slot utilization guideline is at or be low 25%. At higher utilizations, z/OS response times can degrade. And an ext reme local page slot shortage could even cause the z/OS server to crash. The data for the SAS/GR APH plot is from the z/OS Resource Measurement Facility record type 71. The MXG performance databa se (PDB) stores this record information in SAS format in the PDB.TYPE71 dataset |
proc step, proc contents, dataset, macro variable | source | 10 |
| 24 | Applied Revolutionary BI | Charles D. Kincaid | Two years ago, Revolutionary BI described a way to bring business analytics reporting into the twenty-first century. By taking advantage of internet usage patterns developed outside of the corporation (as learned from Google, Twitter, Facebook and many more), the business analytics paradigm inside the corporation can achieve a new level of capability. However, to take these revolutionary ideas into the corporate cubicles, many of the technical and process details within SAS and between SAS and other technologies have to be worked out. This paper presents ideas, suggestions, and questions that start the conversation for implementing Revolutionary BI in your organization | sas enterprise guide, filename statement, business intelligence, excel, data science | source | 16 |
| 25 | Assembling Data Sets for Use in Geographic Information Systems (GIS): Use of PROC TRANSPOSE in Macros | Saiful Momen | With the increasing popularity of Geographic Information System (GIS), outputs of data analyses are often mapped, and analyzed for spatial patterns. The format of GIS files requires that the data set to be joined with map features contain observations (i.e. rows) corresponding to each spatial feature (e.g. a county, or a hospital). Often published data sets, particularly the ones that come out at regular intervals in the same tabular format (e.g. employment data by county) do not have rows representing the spatial features. This paper shows a simplified case of a project that used BASE SAS to assemble a "GIS-ready" data set out of multiple data tables that were published over the years on sector-wise (i) employment and (ii) gross output, and disaggregated by districts in Bangladesh. Source files were in multiple MS EXCEL files with multiple worksheets |
excel, proc transpose, proc step | source | 8 |
| 26 | At Your Service: Your Roadmap to Support from SAS | Kathryn A. Council | At your service. How do you make the most of the products and services from SAS Publishing, SAS Education, SAS Technical Support, and support.sas.com? You have world-class support from SAS at your fingertips. But where do you start? How do you navigate the sea of information available to you from SAS? How do you find the resources you need to do your job? This paper will provide you with practical tips, tricks, and techniques to find exactly what you need to use SAS. You'll save time, learn about upcoming content and, best of all, become a more proficient and expert user of SAS. This paper will benefit the new user and the seasoned SAS user alike. The intended audience is anyone interested in learning more about how to effectively use the services available from SAS | source | 6 | |
| 27 | Automatically Generating Highly Customized Microsoft Excel Reports with SAS Macro and Integration Technologies (DDE) | Helen Fu, Elayne Reiss, Meghal Parikh, Sandra J. Archer |
As a fast growing large public metropolitan research institution, the Office of University Analysis and Planning Support at the University of Central Florida (UCF) is challenged with transforming very large data sets into information that will support strategic planning and decision making. This paper describes a process that utilizes SAS macro language and integration technologies with Microsoft EXCEL, to generate a series of highly customized EXCEL reports in an automated approach. This methodology allows SAS to process a data set of over 400,000 records, consolidate the data, open pre- formatted EXCEL templates, and write the data to designated data ranges. The integration technology used, called Dynamic Data Exchange (DDE), reduces report generating time by up to 90%, compared to prior methods that used EXCEL PivotTables and manual copying and pasting of results, while improving report accuracy by reducing human error |
excel, put statement, macro facility, macro definition, data step, proc step | source | 11 |
| 28 | Automatically Identify Formatting Problems with Numeric Variables | Wei Xu | Have you ever thought about how to identify formatting problems with numeric variables ? When you analyze all formatted numeric variables in a data set and identify any variable whose entire range of values cannot be displayed with the assigned format . These variables cause SAS to produce the following message: At least one W.D format was too small for the number to be printed. This paper will describe a simple straightforward way to write a message to the SAS log for all problematic variables. The macro GETLST uses PROC CONTENTS to generate a data set containing the format width and decimal values for each formatted numeric variable. A subsequent DATA STEP stores this information in macro variables. PROC SQL is then used to determine the magnitude of the minimum and maximum values of each variable. This information, as well as Boolean indicators for positive and negative values is stored in macro variables |
numeric variable, macro variable, proc step, proc sql, sas log, proc contents, %if-%then-%else, macro definition | source | 3 |
| 29 | BASE SAS Implementation of Information Theoretic Feature Selection for Neural Networks | Martin B. Jetton | In "Neural Network Modeling using SAS Enterprise Miner", Matignon lists' one of the disadvantages of neural network modeling as 'No Universal Input Variable Selection Routine" (page 152). Dr David Scarborough and Bjorn Chambless (2001) established the use of Information Theoretic Feature selection in pre-employment application neural network modeling. Information theoretic entropy provides a convenient metric for estimating the difference between distributions. This paper describes the development of a feature selection algorithm implemented in BASE | feature selection, neural network, proc freq, proc step, predictor variable, sas base, interval variable, sample | source | 13 |
| 30 | Base SAS Methods for Building Dimensional Data Models | Christopher W. Schacherer | As the volume of data available from operational systems continues to grow, dimensional models are becoming an increasingly important analytic tool for enterprise reporting and analysis. Although there are a number of software packages specifically designed for transforming data from operational systems into dimensional models, many smaller organizations find themselves unable to make the significant investment in new technology and personnel necessary to utilize these tools. Many of these same organizations, however, use Base SAS as an analytic tool. Especially for these organizations (but also for larger organizations with more sophisticated business intelligence systems), the current work provides an example of using PROC SQL, SAS/ACCESS, and SAS hash objects to extract, transform, and load data from an operational system into a dimensional data model |
hash table, fact table, proc sql, foreign key, dimension table, proc step, sas library, surrogate key, sas base, key, data standard | source | 12 |
| 31 | Becoming a Better Programmer with SAS Enterprise Guide 4.3 | Andy Ravenna | Both existing and new users of SAS are turning to SAS Enterprise Guide to write and run their code. Long-time users are accustomed to typing all their code in the Program Editor window and simply clicking Submit. New users do not have this same set of expectations and are more willing to point and click on occasion. But the truth is becoming clear; the winning programmer will be the one who has the expertise to use the best of both worlds-either coding or clicking, depending on which is more efficient for a given task. SAS Enterprise Guide 4.3 contains new functionality that can help anyone become a better programmer. This paper addresses the all-important question: When is it appropriate to code, and when to click? The purpose here is to expose new users as well as those familiar with SAS to tips and best practices that will enable them to return to the office as better programmers | sas enterprise guide, proc step, excel, dataset, proc transpose, substr function, sas programmer, work library | source | 40 |
| 32 | Best Practice Implementation of SAS Metadata Security at Customer Sites in Denmark | Cecily Hoffritz | Is it possible to design and implement metadata security so that it can be managed without you creating security conflicts? Do you need to become a metadata security guru before you dare modify the security implementation? This paper provides you with the proper insight so that you can design and implement metadata security at your site with a 100 % guarantee that you will not experience metadata security conflicts in the future. For a number of years, Danish customers and others abroad have benefitted from a well-defined and documented metadata security best practice that is now the de facto standard for security implementations in Denmark. During this paper you are introduced to the "golden rules" of the best practice and to the steps that you need to take when you design your security setup. You are also presented with an example of a security design that has suited the requirements of most of our customers | access control, business intelligence, sas enterprise guide, sas management console, data warehouse | source | 12 |
| 33 | Best Practices for the SAS Scoring Accelerator for DB2 | Matthias Nicola, Kim Sherrill |
The SAS Scoring Accelerator for DB2 enables you to deploy scoring models as user-defined functions in DB2. You can then use these functions in SQL statements to perform scoring inside DB2, directly on existing database tables. This paper presents best practices for DB2 database administrators and for developers that write SQL queries with scoring func tions for a DB2 database. We provide performance guidelines for using the SAS Scoring Accelerator in a partitioned DB2 database (IBM InfoSphere Data Warehouse) as well as tips for writing efficie nt SQL with scoring functions. The topics include result set handling, SQL coding, fenced vs. unfence d functions, logging, database and table partitioning, and workload management | proc sql, data mining, order by | source | 10 |
| 34 | Best Practices in Data Integration: Advanced Data Management | Nancy A. Rausch, Michael Ames, David Barkaway, Jeff Stander |
The success of every business activity-from supplier management to customer service-is dependent upon how well an organization manages its critical data. This paper discusses best practices and capabilities offered by the SAS Data Integration products for planning, implementing, and deploying a production end-to-end data management environment to support intelligence initiatives across an enterprise. In addition, considerations for advanced data management and high-performance computing are covered | proc sql, data merging, parallel processing, stored procedure, spk file, data integration, fact table, master data management, sql server, staging area, sql pass-through | source | 15 |
| 35 | Best Solutions for Tuning Performance of ETL Jobs in SAS Data Integration Studio | Qingfeng Liang | SAS Data Integration Studio is a great tool for bu ilding and maintaining data warehouses and data mar ts. The performance of the extract, transform, and load (ET L) job is critical for building data warehouses and data marts. This paper discusses the time-consuming data transf ormations related to ETL processes in SAS Data Inte gration Studio. The performance for each data transformatio n is benchmarked and compared. The best solutions t o speed up ETL job performance are discussed in detail. The paper also suggests the best practices for designi ng efficient ETL jobs | proc sql, cpu time, data merging, lookup, order by, data mart, proc sort, proc step, benchmarking, data warehouse, hash table, sas base, memory usage, data cleaning | source | 10 |
| 36 | Beyond the Basics: Advanced REPORT Procedure Tips and Tricks Updated for SAS 9.2 | Allison McMahill Booth | This paper is an update to the 2007 SAS Global Forum Paper "Beyond the Basics: Advanced PROC REPORT Tips and Tricks"-a compilation of tips for producing desired PROC REPORT output with SAS 9.1.3. This paper offers tips and tricks for getting desired PROC REPORT output with the new functionality and enhanced features that are specific to using SAS 9.2, such as: exploring the new SPANROWS option for repeating group and order variables on a continuing page using the ODS TAGSETS.RTF statement with PROC REPORT using the new border control for creating gridlines in all destinations changing nodes in an Output Delivery System (ODS) PDF bookmark by using the DOCUMENT procedure Other new topics include: conditionally executing a LINE statement changing the style attributes of spanned headers adding text at the end of a report, even with a PAGE option taking control of across variables Sample code and output are included with each topic |
proc report, proc step, ods pdf, sas output, ods html, ods rtf | source | 18 |
| 37 | BI Competency Center 2.0 Putting the Model into Practice | Rocket Wong, Ian P. Healy |
Maine Medical Center has created a BI Competency Center model to champion Business Analytics throughout the organization. Virtual teams are created and a BICC collaboration model are defined at the start of every project, allowing for increased efficiencies in planning, research, development and data validation. This paper delves deeper into the BICC as a project planning and execution method, as well as shares the benefits of a virtual collaboration model to deepen the role of analytics within an organization | business intelligence, data cleaning | source | 8 |
| 38 | Bivariate Count Data Regression Models: A SAS Macro Program | Nan-Ting Chou, David Steenhard |
Bivariate count models are used in situations where two count dependent variables are correlated and they need to be jointly estimated. Most research on count data regression models focus on univariate cases where the single dependent variable takes on non-negative integer. While Chou and Steenhard (2009) provided a SAS macro program that handles a wide variety of univariate count data distributions, no study has provided a SAS program to estimate bivariate count regression models. This paper develops a SAS macro program jointly estimate two correlated count data series---the bivariate count regression model. Our SAS macro allows for ten bivariate count data distributions: bivariate Poisson, bivariate Poisson-LogNormal, bivariate negative binomial, bivariate generalized Poisson, bivariate Poisson inverse Gaussian, bivariate Borel-Tanner, bivariate Neyman Type A, bivariate generalized Waring, Poisson-Laguerre polynomial, and bivariate Poisson series expansion | poisson distribution, regression analysis, correlation, binomial distribution, likelihood function, model validation, poisson regression, akaike information criterion, negative binomial regression, bayesian information criterion, response variable, zero-inflated negative binomial, predictor variable, estimate, binary variable, polynomial regression | source | 10 |
| 39 | Breaking Down the Silos: Data Management Across the Enterprise | Adrian Jones | Most organizations have a legacy of data distributed around the organization, often held in disparate silos in what appears to be an unco-ordinated way. Traditionally, the business user would acquire the data through an ad-hoc process and often replicate more data to solve the business problem. This leads to many problems ranging from inability to access the right data through to lengthy delays obtaining the needed data. Much of the traditional approach focused on bringing the data to the user, and this is where the problems began. There are many technologies and techniques available to the SAS analyst to exploit the data around the organization. This paper looks at joining the dots between the different storage technologies within the organization and outlines the key areas to be considered when trying to address the problems encountered with data management in the organization | proc sql, sas data integration studio | source | 8 |
| 40 | Building a Good Foundation with Functions | Deb Cassidy | A major advantage of SAS over some other languages is the vast number of built-in functions that make it very easy to do a task. SAS provides over 450 such functions. This presentation will include an overview of the structure of functions, some examples of commonly used functions and some pitfalls that often trip a new user. You'll also learn where to go to learn even more about the available functions and what they do. So, if you want to learn a quick way to sum fields that might have missing values, extract part of a text field or just get an idea of what else you can do with functions, then this presentation is for you | missing value, sysdate9, numeric variable, sample, character variable | source | 10 |
| 41 | Building a Self-Service SAS Reporting Application: Soup to Nuts | George G. Mendoza | SAS is widely used in a number of industries for producin g different types of reports including financial reports, participation reports, outcomes reports, sales reports etc. Ve ry often high volumes of reports are required with a very short turnaround time. Self-service is a key to su ccess in such an environmen t. Self-service reports are generally of higher quality, less labor-intensive and inst antaneous. They also have the potential to reach wider audiences over time. This paper will describe the proces s of building a self-service SAS reporting application using Base SAS together with SAS Integration technologies. The entire process will be described from start to finish with several code samples along the way. So sit back and enjoy the ride! | sample, macro variable | source | 10 |
| 42 | Building Better Macros: Basic Parameter Checking for Avoiding "ID10T" Errors | Matthew T. Karafa | No matter how well you document what a macro's parameters are supposed to accept, at least one user will ignore your hard work and try something unanticipated. This presentation describes my parameter checking scheme, which provides such users direct feedback via the SAS log. This feedback looks like a standard SAS error message, and it gives meaningful explanations of what went wrong. Thus, it should prevent the macro author from having a long debugging session, only to conclude that the user passed the macro inappropriate information. This presentation should be applicable to both beginning and advanced macro coders alike | error message, macro variable, macro function, proc sql | source | 4 |
| 43 | Building Enterprise Applications Using SAS Real-Time Services | Falko Schulz | SAS Web services can help you meet the challenges of integrating the processes in your service-oriented architecture (SOA). Real-time services allow you to leverage the power of SAS across the enterprise and beyond. This paper demonstrates how to create SAS Web services and how to call these Web services from a third-party application. Increase your return on investment by integrating more processes in your SOA with SAS Web services. Note: A zip file with code examples from this paper is available at http://www.sascommunity.org/wiki/Building_Enterprise_Applications_using_SAS_real-time_services | web service, sas management console | source | 12 |
| 44 | Building Match Code Using SAS | David Li | Business analysis frequently entails joining data from different sources. Oftentimes, the linkage must be made based on a customer's name and address data. A SAS name and address match code can accomplish this task and move the project forward without interruption. This paper highlights three concepts: (1) modular design, (2) looped sequential processing, and (3) the parsing of data elements. SAS macros can compactly implement the basic match code. They organize the code modules and perform a sequence of tasks against a single input record at a time. Macros also manipulate the parsing and transforming of text strings into the final match code. The programming techniques necessary to move a data element through the various stages are discussed in detail | macro variable, do loop, macro definition, call symput, dataset, proc sql, sample, create table, data step, %if-%then-%else | source | 9 |
| 45 | Building Provider Panels: An Application for the Hash of Hashes | Judy Loren, Richard A. DeVenezia |
Did you know that an element of a hash table can be another hash table? How cool is that? Kind of like a multidimensional array on steroids. You don't need to know in advance how many elements you will have nor how deep they will nest. Furthermore, you can do this without a single macro variable! The "hash of hashes" technique provides one solution to a classic challenge in healthcare: how to process claims data to determine which provider within a given specialty is most likely overseeing a patient's care. The algorithm requires examining an unpredictable amount of information and making decisions at defined breakpoints. This paper will demonstrate the code for a "hash of hashes" solution and compare it to two other approaches: the double DOW and SQL. Along the way we'll make use of some features of hash tables that are new in 9.2. If you love hash tables, you'll flip for the hash of hashes! | hash table, proc sql, hash iterator, do loop, sample, proc freq, hash of hashes | source | 9 |
| 46 | Bump Hunting With SAS: A Macro Approach to Employing PRIM | Jennifer Sniadecki | Many learning-based data mining methods can be used to classify particular subgroups of interest. One such method, the patient rule induction method (PRIM), is used to identify subgroups with extreme values of the target variable (i.e., outcome or dependent variable). This paper examines the PRIM algorithm and its uses, provides an example with data, and discusses SAS code that can be used to generate the PRIM algorithm | response variable, missing value, numeric variable, nominal variable, sample, macro definition, data cleaning, data mining | source | 9 |
| 47 | Can SAS Do Canvas? Creating Graphs Using HTML 5 Canvas Elements | Edwin J. van Stein | With the introduction of the canvas element in the HTML 5 specifications, an interesting alternative for creating nice- looking graphs that are compatible with most Web browsers has become available. The purpose of this paper is to provide an introduction to the canvas element and how to let SAS draw on it. The examples that are given have been tested in SAS on Microsoft Windows and UNIX with different Web browsers on Windows, Linux, and Mac OS X as client | ods html, line chart, bubble plot | source | 12 |
| 48 | Cash Flow Modeling for Fixed-Income Securities: A New Approach Using the SAS Risk Management for Banking Solution | Sunny Zhang, Jeff Hasmann, Thomas Kimner |
Cash flow modeling is critical to valuation and analysis of fixed-income securities including ABS, MBS, and CDO. Traditionally, due to the complexity of cash flow structure of various loans and receivables, the cash flow modeling and validation process is often a black box and is difficult for the investors to understand how their portfolio performance and securities values are associated with, and impacted by, the performance of underlying assets such as credit card receivables or mortgage. As a result, the investors cannot obtain the insights about the future performance trend of the assets to effectively design and execute their investment and risk mitigation strategies. This is also one of the lessons learned from the financial crisis. The paper describes a new and transparent approach that utilizes SAS Risk Management for Banking to address the above issues | sas risk, pd, credit card, interest rate, default rate, sample, numeric functions, recovery rate, correlation, proc step, monte carlo | source | 14 |
| 49 | Choosing the Road Less Traveled: Performing Similar Tasks with Either SAS DATA Step Processing or with Base SAS Procedures | Kathryn McLawhorn | When you are seeking a solution that requires SAS code, do you choose the path that is marked with DATA STEP programming? Or, do you prefer the path that applies Base SAS procedures? Programmers often tend to use just one path, based on their programming skills and personal preferences. In many situations, you can use either DATA STEP logic or procedure code to arrive at the same solution. To help broaden your horizons, this paper provides examples of common situations in which using either method leads to the same result. While this paper compares coding methods, it does not promote one particular method nor examine differences in efficiency. The discussion is aimed at intermediate to high-level users. Whether you are a loyal DATA STEP coder or a devoted proponent of procedure syntax, this paper offers some alternative techniques to help diversify your programming knowledge and skills |
proc step, by group processing, proc report, proc tabulate, proc print, sas base, proc freq, numeric variable, class variable, proc transpose, proc means | source | 12 |
| 50 | COMMAND Your Session: SAS Commands and Command-Style Macros | Nigel B. Neely | SAS commands are used to accomplish various tasks within an interactive SAS session. Often, users issue commands and are completely unaware of the command that has been executed. One such example is pressing the print icon on the toolbar. Once this icon is pressed, the active window is printed to the default printer location by submitting the command DMPRINT from the toolbar. When users become familiar with SAS commands, commands can provide a flexible, efficient way to accomplish an array of tasks. Perhaps most importantly, users are not limited to the commands provided in the product installation. The macro facility provides an environment where users can create their own commands to carry out highly customized tasks | sas log, macro facility, user-defined format, proc contents, proc step, macro definition | source | 8 |
| 51 | Compare the Effectiveness of Different Methodologies in Prediction of Dichotomous Outcome | Jimmy Fang | Dichotomous outcomes widely exist in nature including death or live, disease or no disease, success or fail, etc. Many methodologies could be developed to forecast a same dichotomous outcome. The difference among the methodologies may include explanatory variables, post-model adjustment, or even various model functions. The area under the receiver operating characteristic (ROC) curve, or its equivalent c statistic, is the most important parameter to assess the effectiveness of the prediction. In this paper we describe how to get ROC curve and c statistic from the validation data and compare the effectiveness among different methodologies. We provide two ways to make ROC curve and calculate c statistic based on the observed events and the predicted probabilities from different methodologies. One way is to transfer the predicted probability to logit and then fit the logit with the observed outcome using PROC LOGISTIC. Another way is to do the basic calculation using SAS macros |
roc curve, proc logistic, logistic distribution, proc step, macro variable, proc gplot, macro definition, predictor variable | source | 8 |
| 52 | Comparison of Imputation Methods to Examine Mediator Effect for Social Support of Mothers of Mentally Ill Children | Abbas S. Tavakoli, Kathleen Scharer, James Hussey |
This study compares imputation methods (single and multiple) to examine the role of perceived stress in the relationship between social support and mood, and tested whether mediator effects influenced the relationship. The cross-sectional data reported here was collected in an experimental design with repeated measures with mothers of children who had been hospitalized in a child psychiatric unit. These methods included no imputation, single imputation, and multiple imputation for missing values. The results did not indicate any mediator effects for coping in the relationship between perceived stress and mood. These results were similar when imputation and no imputations methods were used. However, researchers should consider using imputation results were similar when imputation and no imputations methods were used. However, researchers should consider using imputation methods to help improve problems caused by missing values in the study | imputation, missing value, predictor variable, response variable, multiple imputation, effect size, regression analysis | source | 7 |
| 53 | Comparison of Probabilistic-D and k-Means Clustering in Segment Profiles for B2B Markets | Dipanjan K. Dey, Satish Garla, Goutam Chakraborty |
Customer segmentation is a critical business analysis tool that allows organizations to build customer profiles and plan marketing efforts to satisfy the varying demands of different segments. The objective of the present study is to empirically explore the concept of probabilistic -D clustering for segment profiling in a business -to-business (B2B) market. A SAS macro that can be used on any data set for application of this technique is developed and reported. To the best of our knowledge, probabilistic-D technique has never been empirically tested in a business environment. It was compared with the widely used k - means clustering technique. Findings indicate a better explanation of customer segment profiles using probabilistic -D clustering because k -means seems to f orce a high percent of observations in segments where cluster membership probabilities are less than 50% as calculated by probabilistic -D clustering macro | cluster analysis, data mining, sample, euclidean distance, customer segmentation, score code, macro definition | source | 9 |
| 54 | Conditional Classes and Pattern Recognition in SAS | Scott Hanson | Conditional classes are data objects that do not have tidy rules of membership. Their characteristics make up, more or less, the possibilities of membership rather than probabilities. How one thinks about conditional classes is critical. Complexities of pattern recognition, an area that has received strong practical implementation in SAS, will serve to illustrate questions of assignment, data ambiguity, and data objects that do not have precise relation and certain membership | missing value, sas function | source | 6 |
| 55 | Conditional Processing without Macros | John F. Ladds | SAS has some powerful tools to handle conditional program flow but you may not want all that power or the learning curve that goes with it. You may only want to turn on special code for debugging, error handling or in special cases. The RUN statement, with its optional argument CANCEL, can help create conditional processing routines without the need for more involved SAS tools | macro variable, proc print, run, cpu time, proc step | source | 5 |
| 56 | Confessions of a Clinical Programmer: Dragging and Dropping Means Never Having to Say You're Sorry When Creating SDTM Domains | Janet E. Stuelpner, Jack Shostak |
We have been clinical programmers for many years. Our first instinct has always been to write the SAS code by hand because that is all we had in the past . That meant knowing a great deal of syntax and always having the manuals handy. It also meant pages and pages of code that were difficult to correct, difficult to maintain and hard to reuse for different compounds or devices . The first level of growth came when SAS introduced various windows and wizards (e.g., Import/Export Wizard, Report Window, Graph -n-Go) that gave us the ability to start using the wizard and then grab the SAS code and change it as necessary. The next innovations from SAS were tools like Enterprise Guide and Clinical Data Integration with their Graphical User Interface (GUI) that made programming a great deal easier, faster and much more efficient. You can still access all of the different data sources that you need from SAS data sets, spreadsheets and/or relational databases, but in a much easier way | sas base, sas enterprise guide, sdtm domain, proc sql, sas programmer, proc step, proc sort, clinical trial, data governance, data validation rules, raw data, dataset, excel, data cleaning, cdisc | source | 12 |
| 57 | Considerations for Implementing a Highly Available or Disaster Recovery Environment | Diane Hatcher, Jochen Kirsten |
It is a fairly common practice to clone your SAS9 metadata-based environment to create a backup for disaster recovery purposes. We discuss what you should consider when designing your backup strategy. The strategy could also have implications on how you should configure your production environment | sas grid, proc step | source | 13 |
| 58 | Consulting: Career Path Considerations | Kirk Paul Lafler, Charles E. Shipp |
Consulting is an exciting vocation with benefits and also challenges. At what point are you ready to start consulting? At some point, as your SAS programming skills develop, it is wise to plan ahead in your career. While it is an option to retire where you are, there are also opportunities to extend and use acquired skills and talents for consulting during and after a successful career. An excellen t way to explore possibilities is to visit with successful SAS consultants. They can cover specialty consulting, team consulting, book authoring, and many other possibilities to expand and extend your career. We cover briefly the success factors in beginni ng a successful consulting career, but use most of the time to expand on the important considerations in choosing this career path. Considerations will include the timing of preparation and actions for moving into consulting | sas 9, sas consultant | source | 8 |
| 59 | Consumer Data Analytics: The Good, the Bad, and the Possibly Deceptive | William Neafsey, Ted Lang |
Customer Intelligence relies heavily on consumer data, yet surveys are riddled with the absence of "good" data. Regardless of the imperfections, companies need to rely heavily on input from buyers to obtain a measure of the attitudes of the market. Research firms apply various techniques in their sampling and survey methods to ensure they are collecting meaningful responses, including monetary and award offers. For various reasons, respondents, who are not engaged in the topic, still manage to find ways to get around the system to get their responses accepted to collect their survey prize. Because surveys produce a degree of bad data, we believe the analytics stage doesn't begin after data collection; it must absolutely take place during, or immediately following, the data collection phase. Customer IntelligenceSAS Global Forum 2011 Confidential Page 2 3/4/2011 The views and content of this paper do not represent the views of Ford Motor Company or SAS Institute, Inc | sample, standard deviation | source | 11 |
| 60 | CONTRAST and ESTIMATE Statements Made Easy: The LSMESTIMATE Statement | Kathleen Kiernan, Randy Tobias, Phil Gibbs, Jill Tao |
In many SAS/STAT modeling procedures, the CONTRAST and ESTIMATE statements enable a variety of custom hypothesis tests, but using these statements correctly is often challenging. The new LSMESTIMATE statement, available in ten procedures in SAS/STAT 9.22 software, greatly simplifies the use of these statements. The LSMESTIMATE statement enables you to side-step parameterization issues and to specify custom tests in terms of population quantities of direct interest (the LS-means). The LSMESTIMATE statement also implements a new nonpositional syntax for specifying contrasts. This paper discusses these new features and demonstrates them with examples from actual user questions to the Statistical Procedures group in SAS Technical Support |
proc step, proc genmod, proc glm, main effect, logistic distribution, proc glimmix, proc logistic, proc mixed, multiplicity, proc plm, anova, class variable, f-test, fixed effects, proc phreg, odds ratio, two-way anova, interaction effect, proc surveylogistic | source | 19 |
| 61 | Contrast Statements for Binary Responses: Comparing and Contrasting with PROC GLIMMIX | Jeffrey M. Gossett, Mary Aitken, Beverly K. Miller, Christopher J. Swearingen |
SAS provides many useful options for post-hoc estimation within the GLIMMIX procedure. When the response is binary, PROC GLIMMIX can produce estimates that are link transformed, proportions, and odds ratios, depending on the options selected. Using data from a community intervention study, this paper demonstrates how to use post-hoc estimation statements in PROC GLIMMIX to produce estimates and test hypotheses to evaluate the intervention |
odds ratio, proc glimmix, proc step, confidence interval, proc mixed, main effect | source | 10 |
| 62 | Create a Main Program to Run Multiple SAS Programs Utilizing the %INCLUDE Statement | Lisa A. Mendez | Learn a quick and easy way to run multiple SAS programs from one "Main" program. This method provides flexibility and helps with the organization of macro variables that are used across multiple programs. By using the %INCLUDE statement you can retrieve and run external SAS programs. This paper will help you prepare to write a Main program, organize your macro variables and library references, and utilize the %INCLUDE statement |
macro variable, libname statement, sas log | source | 4 |
| 63 | Create a Microsoft Excel Pivot Table Using DDE | Kevin Chung | Pivot Table is one of the practical and prevailing features in EXCEL. It can be used to summarize, analyze, and investigate the raw data in an effective and efficient fashion. Several presentations with respect to this topic by using VB scripts method have been introduced at SAS conference in the past couple years. This paper provides an alternative approach, SAS FILENAME with DDE method as well as EXCEL PIVO T functions, to create a custom formatted Pivot Table in EXCEL application. Unfortunat ely, DDE is only available on Windows environment. The author also provides an easy way to transfer SAS data set from Unix to Windows platform |
excel, boolean, macro variable, raw data | source | 10 |
| 64 | Create compelling visualizations with geographic data and JMP 9 | Jeff Perkinson | JMP 9 introduces exciting graphical support for geographic data. In this presentation, you learn how to use the built -in background maps in any plot of geographic data. You also see how to connect to a Web mapping service to display specialty maps like satellite images, radar images, or roadways. Because JMP 9 can plot geographic shapes based on place names, we demonstrate h ow to use the flexible architecture to create your own shape files to plot any location, like a floor plan or a campus. Lastly, we show how to convert SAS map data sets for use in JMP | dataset | source | 10 |
| 65 | Creating a Half Error Bar Using Graph Template Language | Dongsheng Yang | Many statistical ODS graphics in SAS 9.2 are generated by the Graph Template Language (GTL) using the predefined graph templates in the SAS 9.2. With GTL, we can also create our own graph template. The half error bar (mean ± standard error) is a popular plot in displaying statistics. However, the major problems in creating a standalone half bar are to dynamically change the direction of the half bar to avoid overlay, display the number of subjects over time at the bottom of the plot, and minimize user inputs. In this paper, we show how to use GTL to create a standalone half error bar plot. We created a macro called Halfbar(). The user provides the Halfbar() macro with the data set name, group, x, y, and graph parameters such as axis labels and ticks, etc. The macro will output a nice-looking half bar plot. We also show if you specify a customized ODS style such as the journal style and call the macro, you can easily generate a high-quality plot for publication |
graph template language, bar chart, macro parameter, scatter plot, proc step | source | 11 |
| 66 | Creating a Microsoft Excel Report: A Comparison of the Different Techniques | Romain Miralles | Many techniques exist to create an EXCEL file using SAS. Each of them offers some unique advantages. LIBNAME statement is a new and useful option availa ble with SAS 9 but old methods like DDE are still v ery powerful and offer some unique capabilities. Knowing and und erstanding the different techniques is essential fo r SAS programmers to quickly and effectively produce a report that will meet the requirement provided by the customer. This paper will list and describe the different tec hniques to generate an EXCEL file from SAS, and pro vide recommendations on the appropriate method to use when an EXCEL output must be created |
excel, libname engine, proc export, proc step, name range list, ods csv, ods html, sas programmer, ods excel, dataset, xls, macro definition, label statement, sas 9 | source | 12 |
| 67 | Creating a SAS Model Factory Using In-Database Analytics | John Spooner | With the ever-increasing number of analytical models required to make fact-based decisions, as well as increasing audit compliance regulations, it is more important than ever that these models be created, monitored, retuned, and deployed as quickly and automatically as possible. This paper, using a case study from a major UK financial organization, shows how organizations can build a model factory efficiently using the latest SAS technology that uses the power of in-database processing. It focuses on how to integrate SAS Enterprise Miner, SAS Model Manager, and SAS Data Integration Studio, while leveraging the power of highly performant database applicances such as Teradata and Netezza | sas model manager, data mining, model deployment, batch processing, champion model, in-database processing, model monitoring, data governance, data wrangling | source | 8 |
| 68 | Creating a Table of Contents for Microsoft Word Using AutoFormat and Cascading Style Sheets | Fareeza Khurshed | This paper demonstrates how to create a table of contents (TOC) with SAS TITLE statements, cascading style sheets (CSS), and Microsoft Word. The CSS mimic Microsoft Word styles allowing the Microsoft Word AutoFormat to classify them as appropriate Microsoft Word styles. Once the styles are implemented, built-in Word functionality can be used to create a TOC containing the appropriate page references and hyperlinks to the relevant sections | title statement, by group processing, sas enterprise guide, output delivery system | source | 7 |
| 69 | Creating Custom Web Applications with SAS 9.3 Stored Processes Using SAS AppDev Studio | Bruce L. Faulkner, Virgil Q. Sealy |
This paper explains how to use SAS AppDev Studio to create custom Web applications that will run SAS stored processes on a SAS 9.3 EBI server | sample, macro variable, sas management console, sas log, sas enterprise guide | source | 19 |
| 70 | Creating Proportional Venn Diagrams Using Google and SAS | Hillary Kruger | This paper presents a simple process to create proportional Venn diagrams within SAS using the API for Google charts. Many features are available to create a Venn that suits any need | data visualization, proc freq | source | 6 |
| 71 | Creating Reusable Programs by Using SAS Enterprise Guide Prompt Manager | Angela M. Hall | The Prompt Manager in SAS Enterprise Guide is an excellent mechanism to create flexible and reusable projects. Advanced programmers can translate complicated code tasks and transform these tasks into a project that any level of SAS Enterprise Guide user can execute. This paper includes an example of a data upload and cleaning process. To allow novice SAS Enterprise Guide users to utilize and complete the routine process efficiently, the process in the example was simplified via code modifications and prompts | sas enterprise guide, sas enterprise guide project, dataset, missing value | source | 12 |
| 72 | Creating Stylish Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS | Vincent DelGobbo | This paper explains how to use Base SAS 9 software to create multi-sheet EXCEL workbooks (for EXCEL versions 2002 and later). You learn step-by-step techniques for quickly and easily creating attractive multi-sheet EXCEL workbooks that contain your SAS output using the ExcelXP ODS tagset and ODS styles. The techniques that are presented in this paper can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real time using SAS server technology is discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented |
excel, xls, output delivery system, sas output, proc report, proc template, sas base, proc step, by group processing, ods xml, sample, proc format, dataset | source zip |
22 |
| 73 | Creating Word Clouds in SAS: A DSGI Approach | Murphy Choy | With an increasing amount of unstructured information on the internet, data visualization of such information is fast becoming an important part of data analysis. This evolution of graphical approaches to handle unstructured data has led to a new field of analytics known as visual analytics. Newer approaches towards visualization have led to a wide variety of graphical techniques such as word clouds, linkage graphs and geo-visualizations. Many of these techniques have not been incorporated into SAS. However, innovative use of SAS Macro in conjunction with the use of SAS DSGI can enable users to exploit the power of visual analytics | unstructured data, sas visual analytics, proc freq | source | 8 |
| 74 | Custom Sequence of Rows and Columns in PROC TABULATE | Erik W. Tilanus | PROC TABULATE can order class variables in rows and columns in several ways. Internal order or formatted order are commonly used. Internal orders according the internal value of the variable, numeric or alphabetic and formatted order does the same on the formatted values. But what if you want to order your columns in a specific way, which is neither internal nor formatted? A simple trick using PROC FORMAT can help you out |
proc tabulate, proc step, class variable, proc format | source | 4 |
| 75 | Data Integration Monitor | Bart Heinsius | The Data Integration Monitor provides a SAS data integration (DI) site a browser-based front-end to monitor the state and the progress of SAS DI batch processes in real time, quickly discover and solve SAS DI batch problems, and discover historical trends and potential bottlenecks in SAS DI batch processes. In addition, it provides a facility for gathering audit statistics for data quality and data governance purposes | sas data integration studio, elapsed time, sas log, data governance, data cleaning, batch job, cpu time | source | 10 |
| 76 | Data Mining in SAS with Open-Source Software | Zhengping Ma | It is common in many industries for data to exist in SAS format. Statisticians are often more familiar with the SAS programming environment in comparison to other systems. Therefore, SAS Enterprise Miner is usually the first choice for data mining projects under such circumstances. On the other hand, WEKA is a data mining suite which uses open source code and is available free of charge. More importantly, it offers opportunities to modify the source code for algorithm customization. It also re-implements many classic data mining algorithms, including C4.5 which is called J48 in WEKA. An additional advantage of using WEKA is that it can run hundreds of variations of a model with command mode or customized coding, which is often necessary in research and key for developing reusable tools. This paper presents ways to connect SAS to WEKA and R and to run packages defined outside SAS for data mining projects | data mining, proc hpsplit, roc curve, sample, data step, raw data, sas programming | source | 11 |
| 77 | Data Mining Using JMP | Murphy Choy | Data mining is increasingly adopted by companies who seek to maximize their overall operational efficiency and profitability. However, most companies are unwilling to invest huge capital to procure data mining software. With the worsening economy and drying up of credits and capital, companies are hard pressed to come up with the capital for such facilities which can help them streamline their process and improve the bottom line. JMP is a simple and cheap alternative created by SAS for six sigma and quality control. In addition, it has many features which can be adapted for data mining. In this paper, we will explore some of the options that JMP has to offer to companies who want to do serious data mining at an affordable price | data mining, regression analysis, proc princomp, proc hpsplit, correlation | source | 9 |
| 78 | Deconstructing Barriers for Dashboard on Demand Data | Maureen Murray | As the acceptance and use of the data warehouse has grown so has our users' scope in how to use this now easily accessible data. Recently, a college administrator requested that his technical staff design and develop a dashboard which required enterprise data from both the Student Administration system and the University Financial system. The current phase of development of our data warehouse provides data from the Student Administration system. A university-wide Financials Data Mart, housed on a SQL server, is provided by the university Financial Administration office for reporting and data exploration. Eventually, the dashboard developers decided to employ the SAS Intelligence Platform as their development and delivery tool and requested our assistance during their development effort. This paper details the solutions we built using different components of the SAS Intelligence Platform to support the data acquisition and overcome technical and policy barriers | data mart, dataset, data warehouse, business intelligence, proc sql | source | 8 |
| 79 | Demand Sensing via C-B4 Pattern Analysis and SAS Forecast Server | Irad Ben-Gal | C-B4 Monitoring is a new method for automatically monitoring and analyzing complex processes. At the heart of the C-B4 Monitoring concept is the analysis of data patterns by the C-B4 network. Once the network is constructed, it captures all the significant dynamics and dependencies in the data. This information can be used by various applications. This presentation focuses on the integration between SAS Forecast Server and C-B4's pattern- recognition server, with respect to demand sensing and forecast control. Reference will be given to industrial, telecom, and retail organizations | network optimization, demand forecasting | source | 4 |
| 80 | Designing a Grid Computing Architecture: A Case Study of Green Computing Implementation Using SAS | Krishnadas Nanath | With the growing concerns about environmental impact of computing, it has become necessary to utilize the unused computing resources. It not only ensures efficiency and reduced carbon emissions but also leads to potential cost/time savings in organizations. Intense computing actions that work on huge data, generally take hours to compute which could be reduced to few seconds using grid computing. This paper is an attempt to understand the nature of grid computing and explore the relation between work load and optimum number of nodes required for grid implementation. This would act as guidelines to design an optimal grid for an application of a specific workload. It takes up the case of a computer lab and conducts a pilot study on utilization of computing resources by the students followed by implementation of grid computing using SAS. Paper also comes up with a Green Computing Strategy plan for the computer center (grid computing and SAS being a part of it) | grid computing, sas grid, sample | source | 11 |
| 81 | Detail Forms with SAS Stored Procedures | David P. Wells | Many times, the viewer of a list of data wants detailed information for a few items in the data, but not all the data. In SAS Web Report Studio, it is easy to give drill-in capability to reports, but some reports cannot be done with SAS Web Report Studio. However, reports that are created from SAS Stored Processes can be as detailed or as brief as you want, and can be forced to look like anything you can think up. I allow drilling into data from a stored procedure by linking to a second stored procedure, using parameters in the link statement from the first stored procedure's listing of the data. | create table, group by | source | 4 |
| 82 | Detecting and Preventing Fraud in Financial Institutions | Ellen Joyner | Fraud has evolved from being committed by casual fraudsters to being committed by organized crime and fraud rings that use sophisticated methods to take over control of accounts and commit fraud. The problem is pronounced in the financial sector where the compromises are more sophisticated than in other industries. It is essential to get not only multiple sources of data to understand the entities being compromised but also apply sophisticated analytical methods to understand the data, extract optimal information, use high-end pattern recognition and text mining to create features, and advanced modeling techniques to fit the best possible models to the data to reduce false positive rates. The presentation discusses the challenges faced in fraud detection and how they can be addressed using sophisticated analytical techniques. | fraud management, outlier, case management, card fraud, dealer fraud, financial services, type i error, identity fraud, retail banking, credit card, machine learning | source | 16 |
| 83 | Develop Effective Disease Management Program for Managed Care Organization Using SAS | Qingfeng Liang | Individuals with chronic illnesses account for over 70% of the total health care costs in the United S tates. Chronic illness is extremely costly for health insurance pa yers due to high readmission rates and the large vo lume of care. Therefore, health insurance payers and health care providers increasingly seek to identify strategies to manage this patient population in order to reduce cost and impr ove patient care. Health insurance payers have data from various sources; how to evaluate data to support disease ma nagement initiatives and strategies becomes very ch allenging. The paper focuses on four key processes to develop an effective disease management program. SAS soluti ons are implemented to build (1) Data Integration and Manag ement; (2) High Risk Population Classification; (3) Intervention Automation; and (4) Outcome Evaluation and Manageme nt | health insurance, insurance claims, sas base | source | 8 |
| 84 | Developing User-Defined Functions in SAS: A Summary and Comparison | Songfeng Wang, Jiajia Zhang |
In this paper we review the methods for SAS users to write their own functions. These methods include SAS macro, SAS/IML, SAS Component Language (SCL), and two recently available procedures: PROC FCMP and PROC PROTO. We give examples showing how to make user functions using each method. The computation time of different methods is investigated. The pros and cons of each method are summarized, which can give SAS users basic ideas about choosing the appropriate method according to their own purposes |
proc step, proc fcmp, macro definition, macro variable, sas log, monte carlo, confidence level, proc iml, macro facility, sas function, options statement | source | 9 |
| 85 | Distance Mapping in Health and Health Care: SAS as a Tool for Health Geomatics | Barbara B. Okerson | Geomatics is the science concerned with using mathematical methods with spatial data to learn about the earth's surface. Health geomatics is used to improve the understanding of relationships between people, location, time, and health. Geomaticists assist in discovering and eliminating disease, aid in public health disease prevention and health promotion initiatives, and support healthcare service planning and delivery through defining spatial relationships. Distance mapping is one of many tools in the geomatics toolbox that furthers this understanding. SAS provides a comprehensive set of graphics tools and procedures as part of the SAS/GRAPH product that can be used for distance mapping, including the new GEOCODE procedure and the GEODIST function, available with SAS 9.2. This paper combines the graphics available in SAS/GRAPH with other available SAS tools to explore spatial relationships in the health care arena. Examples include maps, grid squares, area charts, and X-Y plots |
proc step, data mining | source | 12 |
| 86 | Do Guest Reviews Really Matter? Linking Social Media and Operations Data | Kelly A. McGuire | Hotels and casinos have long suspected that guest comments and ratings posted on review sites influence key operating metrics like future bookings. Recent developments in social media analytics have finally provided the opportunity to firmly establish this relationship, and understand the conditions under which the relationship holds. In this session, you learn how to incorporate operational data into social media analytics. We discuss emerging categories of social media analytics for hospitality, which operational data sources are most closely linked to social media, and how to determine whether there is a statistically significant relationship between guest sentiment and future bookings | revenue management, key performance indicator, social network analysis | source | 9 |
| 87 | Don't Gamble with Your Output: How to Use Microsoft Formats with ODS | Cynthia L. Zender | Are you frustrated when EXCEL does not use your SAS formats for number cells? Do you lose leading zeros on ZIP codes or ID numbers? Does your character variable turn into a number in EXCEL? Don't gamble with your output! Learn how to use the HTMLSTYLE and TAGATTR style attributes to send Microsoft formats from SAS to EXCEL. This paper provides an overview of how you can use the HTMLSTYLE attribute with HTML-based destinations and the TAGATTR attribute with the TAGSETS.EXCELXP destination to send Microsoft formats from SAS to EXCEL using the Output Delivery System (ODS) STYLE= overrides. Learn how to figure out what Microsoft format to use and how to apply the format appropriately with ODS. A job aid is included in the paper; it lists some of the most common Microsoft formats used for numeric data. The examples in this paper demonstrate PROC PRINT, PROC REPORT, and PROC TABULATE coding techniques |
excel, proc step, ods xml, proc report, ods html, proc print, date variable, format statement, character variable, sysdate9, user-defined format, numeric variable, proc tabulate, xls, sas output, output delivery system, dataset | source | 30 |
| 88 | Don't Waste Too Many Resources to Get Your Data in a Specific Sequence | Henri Theuwissen | This presentation compares different sorting methods, the SORT procedure, the SQL procedure, and sorting in HASH tables, and compares the results in performance. Tips are given on how to remove redundant sorts by using the SORTEDBY option, the NOTSORTED op tion, or the implicit sort of procedures such as PROC SUMMARY and PROC REPORT. Also, new options in SAS 9.2 enable you to get the data in the required sequence without extra DATA steps. All techniques are presented with examples and resource usage figures |
proc sort, proc step, hash table, proc sql, by group processing, cpu time, dataset | source | 19 |
| 89 | Driving Clinical Safety Reviews with Data Standards | Geoffrey A. Mann | Data standards foster the development of software that permits informed analysis of the data, shortening the path to discovery. Emerging clinical data standards have led to the creation of JMP Clinical, which combines the visual exploration capabilities of JMP software with the statistical rigor of SAS Analytics. Interactive dashboard displays enable clinicians to create sophis ticated visual summaries of events, findings, and interventions, and then to zoom and filter through the data to explore subject level details (e.g. patient profiles). P attern discovery and predictive modeling techniques, used behind the scenes, enable me dical reviewers to explore the study data with a visually driven user interface, lending strong evidence to the value of data standards | clinical trial, adverse events, clinical data, data standard, relative risk, study day | source | 7 |
| 90 | E-Mailing from Your SAS Program to Keep Everyone Informed | Bob Bolen | Have you ever had a long running SAS job that ends early with an error? Wish you would have known about it? Would you like to have the program send the results of a run to your client? We will look at a technique for emailing from your existing SAS code to do just that | filename statement, proc step | source | 3 |
| 91 | Easier than You Think: Creating Maps with SAS Enterprise Guide | Stephanie R. Thompson | Have you ever wanted to display data on a map to add more punch to your analysis? Sometimes seeing things geographically can put things into a completely new perspective. Letting your data tell a story on a map is easier than you might think. A two-step approach with SAS Enterprise Guide is all you need. From using supplied shape files right through customizing your graph, this paper walks you through the process. Some best practices are also included to make sure your customers are impressed. They will never know how easy it really was | sas enterprise guide, missing value | source | 9 |
| 92 | Easy Ways to get SAS Enterprise Guide to Send Tables and Graphs to Microsoft Office Suite, Without Writing Code! | William E. Benjamin Jr. | This paper will demonstrate several ways to output tables, graphs, databases, and files that are in formats that Microsoft products are able to process. All of these methods require no programming, and just use little known or hidden techniques that are available directly from the SAS Enterprise Guide user interface. The focus will be directly on how a new user to SAS Enterprise Guide can prepare and deliver data to the Microsoft Office (MS Office) suite of tools | sas enterprise guide, txt, excel, cdisc | source | 14 |
| 93 | Effectively Implementing SAS Grid Architectures in Conjunction with Non-Grid-Aware Processes | Bill Nasuti | SAS Grid Computing provides enterprise class governance which allows many SAS applications to run within a centrally managed grid infrastructure. Employing SAS Grid Manager affords enterprise organizations the opportunity to utilize their underlying hardware resources more efficiently. In practice, there are many situations where grid nodes are running non-grid aware processes in conjunction with grid aware processes. In order to alleviate this inefficiency, SAS Grid Manager provides a plethora of controls to ensure that resources are governed in an enterprise environment. This process is very straightforward when the governing grid environments are comprised of exclusive grid aware processes. The utilization of non-grid aware processes introduces unique challenges. This paper presents and discusses a viable architecture to improve on grid environments that consist of both grid aware and non-grid aware processes | sas grid, grid computing | source | 9 |
| 94 | Efficient Merging: Creating Format Libraries to Save CPU Time | Tyler Benz | In my line of work, we are constantly reading in and writing out files containing millions of records and dozens of variables. It is not uncommon to receive a file containing a handful of records and be asked to merge it with multiple large files, do some data manipulation and write out the data to a new file. Programs like these tend to take a lot of CPU time and resources to run, so we are constantly looking for better ways to speed up processing time by implementing efficient SAS techniques within our code. PROC FORMAT and its user defined formatting options is one tool that can be used to do just that: SAVE TIME |
proc step, cpu time, proc sql, proc format, proc means | source | 9 |
| 95 | Eliminating Redundant Custom Formats | Philip A. Wright | Custom formats are an invaluable asset to the SAS programmer. Their functionality provides for much more than simply a mechanism for explicitly labeling values in a dataset. There can be, however, a major limitation-the DATA STEP can only accommodate 4,096 formats at a time. It is unlikely that a SAS programmer would generate this many formats in code, but this is not the only method that generates formats. PROC IMPORT and other third party data conversion programs may well generate a distinct custom format for every variable in a data set, and data sets with more than 4,096 variables are not uncommon. Oftentimes, however, these formats can be quite redundant-the same coding scheme was used for many similar variables. Eliminating redundant custom formats may well get you below the 4,096 limit. PROC SQL, PROC CATALOG, and the DATA STEP are the tools that can be used to eliminate the redundant formats |
proc step, user-defined format, proc sql, proc format, macro variable, proc catalog, create table, left join, order by, format statement | source | 8 |
| 96 | Eliminating Response Style Segments in Survey Data via Double Standardization before Clustering | Murali Krishna Pagolu, Goutam Chakraborty |
Segmentation is the process of dividing a market into groups so that members within the groups are very similar with respect to their needs, preferences, and behaviors but members between groups are very dissimilar. Marketers often use clustering to find segments of respondents in data collected via surveys. However, such data often exhibits response styles of respondents. For example, if some respondents use only the extreme ends of scales for answering questions in a survey, the clustering method will identify that group as a unique segment, which cannot be used for segmentation. In this paper, we first discuss the different data transformation methods that are commonly used before clustering. We then apply these different transformations to survey data collected from 959 customers of a business-to-business company. Both hierarchical and k-means clustering are then applied to the transformed data | hierarchical clustering, standard deviation, xls, outlier, excel, data transformation, macro variable, proc means | source | 15 |
| 97 | Enhancing Your Output from SASEnterprise Guide Creating Something a Little Different | Brian J. Bee | In the enhancement of your report output, you are limited only by your imagination! SASEnterprise Guide has become a very powerful, yet easy-to-use, reporting tool in your Business Intelligence applications. In this presentation we will explore some options for enhancing your output, ranging from applying a simple SAS-supplied format, through to some complex conditional highlighting, and the use of the Output Delivery System (ODS). Sample code will be included so you can take advantage of these tools without really having to learn the detail of them if you don't wish to. We will also examine some older techniques that are still in common usage, such as Annotate and DATA STEP Graphics Interface, and some of the latest techniques that have been included in the new SAS Enterprise Guide 4.3 toolkit |
sas enterprise guide, proc step, proc format, macro variable, bar chart, macro function | source | 27 |
| 98 | Enough Really Good SAS Tips to Fill a Book | Arthur S. Tabachneck | You may or may not be familiar with SAS-L, the listserv-based bulletin board where many SAS users have been going, since 1986, to learn more about SAS, help other users, or get answers that they couldn't find elsewhere. Regardless of whether you are familiar with SAS-L, you've probably seen SAS-L posts, as they are often among the | proc step, proc means | source | 7 |
| 99 | Enrich your SAS Reporting with SAS Predictive Analytics | Rob Craige | The SAS Reporting clients have many out-of-the-box capabilities that fully meet most reporting requirements. While most reporting tools are limited to the out-of-the-box capabilities, SAS Reporting can leverage the powerful underlying SAS programming language to supplement those standard capabilities. Think of it as your "get out of jail free" card. This paper shows you how to leverage SAS full predictive analytics capabilities in SAS Reporting including SAS Information Maps, SAS Web Report Studio, SAS BI Dashboard, and the SAS Information Delivery Portal. | predictive analytics, sensitivity analysis, excel, proc sql, proc means, sas enterprise guide | source | 15 |
| 100 | Evaluating the Effectiveness of a Calcium Nutrition Educational Intervention for Middle School Students in Las Vegas, Nevada, Using SAS | Ryan Fernandez | Calcium is an essential mineral that is found and u in the human body. An estimated 1.5 million fractu res occur each year in the United States due to osteopo rosis. The aim of this study is to evaluate the effectiveness of a calcium nutrition intervention for middle school students in Las Vegas, Nevada. The overarching research question is whether the intervention was successful in improving the calcium nutrition knowledge of the students. Data have been collected from pretests and posttests completed by the students. To determine whether each studen t benefited from the intervention, the percentage improvement from pretest to the posttest was calculated. Paired t-tests (PROC TTEST), mixed linear models (PROC MIXED), and frequ ency distributions in SAS 9.2 were used. The SAS code can be seen in the Appendix section at the end of the paper |
proc freq, proc step, proc ttest, proc mixed, standard deviation, paired t-test | source | 13 |
| 101 | Evolution of Linear Models in SAS: A Personal Perspective | Ramon C. Littell | Phenomenal growth in computational power from 1970 through 2010 enabled a parallel expansion in linear model methodology. From humble beginnings in agriculture, linear model applications are now essential in sciences of genetics, education, and biostatistics, to name a few. Indeed, the meaning of "linear models" has evolved accordingly. Developers at SAS Institute have been in the forefront of invention and implementation of these methods at the core of statistical science. Pathways will be traced in steps of SAS procedures, beginning with GLM and REG, proceeding through VARCOMP, NLIN, MIXED and GENMOD, and arriving at NLMIXED and GLIMMIX. Along the way, some problems have disappeared, new ones have emerged, and others are still along for the ride | source | 14 | |
| 102 | Exact Confidence Intervals for Risk-Adjusted Rates Versus Trouble in River City | Ted Clay | When confidence intervals for risk-adjusted rates are based on large-sample approximations, with truncation in case the lower or upper limit falls outside the range of 0-100%, you know there is Trouble in River City. This paper presents an exact calculation of confidence intervals for risk-adjusted rates, applied to the evaluation of hospital performance. From subjects with known probabilities of an event, the method takes the inverse of the binomial distribution generalized to the case of unequal probabilities. Like the logistic model, the confidence interval is calculated assuming that a group effect is additive in the logit domain, causing a shift of the probabilities as a group. When the exact upper tail probability for the observed events is α/2, the mean of the shifted probabilities is the lower (1-α)% confidence limit on the underlying rate | confidence interval, confidence level, poisson distribution, binomial distribution, logistic distribution, normal distribution, sample, proc step, logistic regression, probability functions, regression analysis | source | 11 |
| 103 | ExcelXP on Steroids: Adding Custom Options To The ExcelXP Tagset | Michael J. Molter | The multitude of options available with ODS's ExcelXP tagset has allowed users access to dozens of EXCEL features when creating spreadsheets from SAS, but not all of them. ExcelXP is a SAS-made tool, but because it is a tagset, users have the ability to modify it. In this paper we'll discuss strategies for adding simple functionality to ExcelXP. Users of all levels will not only see the brief, intuitive tagset code used to produce the required XML for these specific examples, but will also realize the power over their output that this and other TAGSETS give them. Those with more experience with XML and tagset coding will learn a little more about the inner workings of ExcelXP as well as general strategies for adding any functionality to any tagset |
excel, put statement, proc step, xls, set statement, macro parameter, proc template, macro loop | source | 14 |
| 104 | Expanding PROC EXPAND | Bruce F. Gilsen | The EXPAND procedure, which is part of SAS/ETS software, provides many types of data transformation for time series data. This paper shows two examples where getting the desired results requires some manipulation of the data before or after use of PROC EXPAND: calculating an un-weighted quarterly average from monthly data and converting weekly data to a monthly average when the weekly data values represent values for seven days |
proc step, time series analysis, time series data, data transformation, date variable | source | 4 |
| 105 | Explicit SQL Pass-Through: Is It Still Useful? | Frank Capobianco | SAS/ACCESS offers two methods of passing SQL to Relational Databases for processing: implicit and explicit. Implicit SQL Pass-Through can be identified by the use of a SAS LIBNAME statement pointing at a relational database. As the name suggests, SAS will attempt to convert such code to SQL that the target database can process. Over time, and especially with SAS 9.2, Implicit SQL Pass-Through has become increasingly powerful. On the other hand, Explicit SQL Pass-Through is a coding syntax that allows the user to write/submit database-specific SQL that SAS will pass untouched to that database. Because of the increased breadth and depth of Implicit SQL Pass-Through, Explicit SQL Pass-Through is no longer required to address many common scenarios. This paper describes those scenarios, as well as cases where (at least for the time being) Explicit SQL Pass-Through is still useful. |
proc sql, sql pass-through, implicit sql pass-through, proc step, teradata, relational database, sas log, create table, sas enterprise guide, libname statement, sas function, sas library, sas programmer, data mining | source | 5 |
| 106 | Exploring Selective Sweeps with SAS | Kristan A. Schneider, Barbara Schneider |
We exemplify how SAS can be used to perform analyses of genetic data. In particular, we perform a population genetic analysis based on a dataset containing microsatellite data in a population of Plasmodium falciparum (the parasite that causes the most virulent form of human m alaria). The microsatellite markers are located around the dhps gene of P. falciparum , which is associated with resistance against the antimalarial drug Sulfadoxine. Resistance-causing mutations are selectively favored and spread in the population. A sele ctive sweep, or the "hitchhiking" effect of an advantageous mutation, refers to the elimination of pre -existing genetic variation. Studying the pattern of genetic variation is important because by measuring the span of selective sweeps we may infer the selective pressures on drug resistance and relate it to the demographic and clinical factors specific to the population under examination | sample, proc freq, relative frequency, excel | source | 11 |
| 107 | Extending SAS High-Performance Forecasting Using User-Defined Models and External Forecasts | Udo Sglavo | For automatic forecasting of large numbers of time series, SAS High-Performance Forecasting (part of SAS Forecast Server) provides access to the most robust models available today. However as an experienced forecaster, you might have the desire to extend the model families provided. SAS High-Performance Forecasting allows you to extend the models in three ways: Custom repositories: You can create your own model repository. External models: Your forecasts are provided by methods that are external to the system. * User-defined models: You are adding forecasting methods that are not provided by SAS High-Performance Forecasting. In this paper, the use of custom repositories, external models, and user-defined models in SAS High-Performance Forecasting will be discussed, and four easy-to-follow examples will be provided | time series analysis, feature selection, proc step, moving average, proc esm, ma, ods graphics, sas function, sas programming, output delivery system | source | 9 |
| 108 | Extending the Reach of SAS Metadata | Edward Lambe | Most companies struggle to provide an end-to-end metadata solution for the data assets they hold. The descriptor portion of a SAS data set can be an important starting point in delivering this solution. By leveraging this data, we can start to provide a complete picture of our data assets. This paper demonstrates the steps required to start building that end-to-end metadata solution, highlighting the use of PHP, Base SAS, SAS ODBC, and FLEX to build a prototype solution | dataset, sas base, dictionary tables | source | 10 |
| 109 | Fighting Fraud in a Pre-Payment Environment | Julie Malida, Greg Henderson |
In the U.S. Health Care industry, dollars lost to fraud each year are 3-10% of total U.S. Health Care spending. Adding in waste and abuse (where the intent to falsify information for financial gain is never proven), these estimates approach 1/3 of total health care spending. In Europe, the fraud estimate is 6% of health care spending, in Canada, 2% of health care spending. It is a worldwide issue. As such, health care fraud is a lucrative business and organized crime, cross-border schemes, and multi-party collusion have moved full force into health care. The health care industry loses 100 times more than the credit card industry does due to fraud, and yet the investment on fraud fighting solutions in health care is one tenth as much as the credit card industry. Most payers of health care claims (private payers as well as governments) are still detecting and investigating fraud after the claim is paid, known as "pay and chase" | source | 11 | |
| 110 | Finding the Best Display Type for Your Data | Varsha Chawla, Lisa Whitman |
With the new release of SAS BI Dashboard 4.3, many new display types are available for dashboard designers to use and take advantage of. Common questions that many dashboard users have will be addressed, including, which display types should be used for various types of data, and how to leverage the different display types so that information is communicated clearly and effectively. Explore the various new charts and displays that can help communicate information, including speedometers, bubble plots, vector plots, waterfall charts, and many other new display types in SAS BI Dashboard 4.3. Learn when to use these displays and how they display your data to create dashboards that are usable, attractive, and efficiently communicate the point you are trying to get across | scatter plot, bar chart, line chart, bubble plot, waterfall chart, histogram, correlation | source | 7 |
| 111 | Finding Your Way through the Wilderness: Moving Data from Text Files to SAS Data Files | Charles E. Mullin | As a SAS software user, you have undoubtedly encountered raw data files that have defied your attempts to read them into SAS and make them into usable SAS data files. Working with such files might make you feel a little lost- like you are wandering around a remote wilderness. This paper helps you to get back on course by presenting solutions to the challenging problems that sometimes occur when you try to read external files into SAS. The paper also addresses advanced data problems and provides creative techniques that enable you to manipulate the data so that it is easier to read. The discussion covers everything from hierarchical structures to the logic that is required for using regular expressions in the input buffer in order to make the data readable | input statement, txt, sample, credit card, character variable, infile statement, dataset, informat statement, missing value | source | 15 |
| 112 | Fit-to-a-T: Using SAS to Create a SAS Global Forum 2011 T-Shirt | Ted Conway | To celebrate the anniversary of the SAS Global Forum 2010 T-Shirt Contest, here's a short SAS program that creates a design for a SAS Global Forum 2011 T-Shirt to commemorate the conference's 36 years of Eureka! moments. And don't worry if you're Photoshop-challenged - the design is generated entirely with Base SAS and | proc step, sas base | source | 10 |
| 113 | Five Ways to Speed Up Your Data Loading Using SAS/ACCESS for Relational Databases | Douglas B. Liming | When moving large amounts of data into a database, the mantra is: Quicker is better. SAS/ACCESS has always leveraged the native database load utilities using the BULKLOAD=YES option. Most users are familiar with BULKLOAD, but here are five additional ways to turbocharge BULKLOAD to make loading data even faster. Even advanced users might not know about some of these undocumented options |
cpu time, db2 access, oracle access, teradata access, label statement, variable, dataset | source | 6 |
| 114 | Flow Isolation in Power-Efficient Traffic-Grooming Problems in Optical Networks | Hui Wang | We address the issue of ensuring the integrity and privacy of communications in optical networks in this paper. For this purpose, the traffic-grooming concept has been extended to include the flow isolation constraints through a multi - class traffic model. We develop ILP formulations and implement and s olve them with SAS/OR OPTMODEL. Our approach guarantees that only traffic components within the same class can be carried on the same wavelength with a relatively small increase in the overall cost |
network demand, uniform distribution | source | 8 |
| 115 | Focused Preparation for SAS Certification Exams | Terry Barham | Preparation is the key to passing SAS Certification exams. Knowing where and how to focus your study efforts can help you reach the required level of proficiency as efficiently and effectively as possible. This paper helps you find and understand the most important details and requirements for SAS Certification exams, including a list of individual test objectives, recommended training and preparation options, exam durations, required passing scores, sample questions, and what to expect on exam day | sample, sas programming | source | 5 |
| 116 | Force Missing Rows and Columns with CLASSDATA in PROC TABULATE | Peter Cerussi | When creating the same report over time, it is important that the format stays the same to ensure consistency. With the TABULATE procedure, levels of a class variable, which do not have any observations, will not appear in the report. To create consistent reports, a solution is needed that will ensure a standard report format over time. In order to account for changes that occur over time, all levels of class variables will need to be included in the standard report format. Creating a SAS data set with all levels of every class variable, along with declaring the CLASSDATA option in PROC TABULATE, will solve this problem. This paper shows how to set up the SAS data set to be used in conjunction with the CLASSDATA option, and how to avoid common errors that will cause PROC TABULATE to error out. The result is a report format that will not change over time when the data changes over time |
class variable, proc tabulate, proc step, dataset, do loop, proc format | source | 5 |
| 117 | From a Doctors Certificate to an International Health Service Database with SAS | Ragna Preal, Tonio Di Zinno |
The Inter Mutualist Agency for Data (IMA) was found ed in 2002 by the seven Belgian National Health Insurance organizations to support the natio nal health services and their partners. The purpose of the agency is to maintain and improve th e performance of healthcare and health insurance in Belgium. Explaining the project called "permanent sample", this presentation shows how the IMA collects and aggregates data from all health services and checks and manipulates the data for research purposes. This pr esentation also explains how the data is made accessible from different locations by different partners for analyses and reporting." | health insurance, sas data integration studio, sas enterprise guide, sample | source | 4 |
| 118 | Genetic Algorithm Optimization for Selecting the Best Architecture of a Multi-Layer Perceptron Neural Network: A Credit Scoring Case | Alejandro Correa, Andres F. Gonzalez, Camilo Ladino |
Neural Networks are powerful tools for classification and regression, but it is difficult and time costly to determine the best architecture for a given problem . In this paper, Genetic Algorithms (GA) are used to optimize the architecture of a Multi-Layer Perceptron Neural Network (MLP) in SAS, in order to improve the predictive power of the credit risk scorecards. The objective function to maximize is the ROC curve and the input variables are the number of hidden layers and units, activation function, use or not of bias and whether it will be a direct connection between the initial and the final layer. Results show that this method outperforms logistic regression and the default neural network architecture of SAS Enterprise Miner . The predictive power of this method is similar to the Global Optimum but in a reasonable time | neural network, logistic regression, data mining, roc curve, logistic distribution, regression analysis, credit risk, credit scoring, default parameter | source | 8 |
| 119 | Getting Personal but Not Too Personal | Stephanie R. Thompson | So, have you tweeted, friended someone, been dug, used a Wiki, blogged, created a network, or shot a video to post online? No? Maybe you think these tools are just for the youth of today and not for the professionally minded. Au contraire. Social media in the broad sense has a wide range of tools that you as a professional can leverage and benefit from. There are some things to look out for since there is a danger of crossing the line between personal and professional. This paper covers some of the more common social media sites as well as some tips on what to put out there for the world to see | data merging, proc sql | source | 4 |
| 120 | Getting Up to Speed with PROC REPORT | Kimberly J. LeBouton | Learning the basics of PROC REPORT can help the new SAS user avoid hours of headaches. PROC REPORT can often be used in lieu of PROC TABULATE or DATA _NU LL_ reporting--two areas that have driven the new SAS user crazy!!! With the added capabilities of ODS, PROC REPORT can look as sharp as an EXCEL report. This paper will show how to use PROC REPORT in both a windowing and non-windowing environment using SAS Version 9 |
proc report, proc step, proc print, proc tabulate, proc means, variable | source | 8 |
| 121 | Giving High School Students the Power to Know: SAS Programming and 21st Century Skills | Sharon T. Jones | SAS has created a tremendous opportunity for high school students to learn skills that involve not only programming but critical thinking and problem solving skills. SAS introduces students to the world of statistics and data but also to the idea that SAS can be used in any industry or situation where analysis is needed to answer questions. This paper discusses the importance of SAS in high schools, implementation, a success story and concludes with a discussion. High school students are given the power of knowledge by applying SAS software in real-world scenarios while increasing their 21st century skills | sas programming, sas base | source | 8 |
| 122 | Global Risk Management: How SAS Enterprise GRC Manages Nonfinancial Risk in a Distributed Enterprise | Victor Jiang, Hao Qiu |
SAS Enterprise GRC (governance, risk management, and compliance) is a SAS solution to help customers automate the management of nonfinancial risks (for example, strategic risks, operational risks, reputational risks, and so on), compliance, and internal audit processes. This paper depicts the scenario of a global bank with several branches in different countries (U.S., China, Saudi Arabia, and Japan): HQ sets up a multilingual environment; clerks in local offices input loss data in different languages; language-specific e-mail messages are sent in the approval process; the loss data are collected and assembled in HQ and finally generated into different language reports | access control, internal audit, data cleaning | source | 17 |
| 123 | Graphing Made Easy with Output Delivery System (ODS) Graphics Procedures | Susan J. Slaughter, Lora D. Delwiche |
New with SAS 9.2, ODS Graphics introduces a whole new way of generating graphs using SAS. With just a few lines of code, you can create a wide variety of high-quality graphs. This paper covers the three basic Statistical Graphics (SG) procedures-SGPLOT, SGPANEL and SGSC ATTER. SGPLOT produces single-celled graphs. SGPANEL produces multi-celled graphs that share common axes. SGSCATTER produces multi-celled graphs that may use different axes. This paper shows how to use each of these procedures to produce different types of graphs, how to send your graphs to different ODS destinations, how to access individual graphs, and how to specify properties of graphs, such as format, name, height and width |
bar chart, phase ii, scatter plot, proc sgpanel, density plot, histogram | source | 18 |
| 124 | Grouping, Atomic Groups, and Conditions: Creating If-Then Statements in Perl RegEx | Toby Dunn | Starting in SAS Version 9, Perl regular expressions were added and confusion soon followed. Since regular expressions are known for writing and not reading data, this is not surprising. Many SAS papers have been written describing what they are, their basic syntax, and giving a few simple examples, but none so far have specifically focused on three primary but puzzling concepts of regular expressions: grouping, atomic groups, and conditionals. This paper will elucidate those topics, starting with their basic definitions and working up to creating an If-Then statement using one line of regular expression code. | prxposn, variable | source | 14 |
| 125 | Hands Free Data Transfer Using SAS | Teo Gamishev | The first step in any data management process is transferring the data from the customer. There are many ways to do that and the most common one is to use proper FTP (File Transfer Protocol) software to navigate to the customer's site and get the data. While this process is fun in the beginning of your relationship with the customer, it may become just another boring step in your data management process. There is a way to reduce that step to a single click of the mouse. This article will show you how to use SAS to transfer data, located on a different type of servers | macro variable, txt, quality control | source | 5 |
| 126 | Healthcare Intelligence: The Challenge of OLAP for Healthcare Data | John Leveille | Some data sources easily lend themselves to aggregation within OLAP cubes. In contrast, when attempting to apply OLAP technology to healthcare and pharmaceutical data sources, even the simplest set of data can leave you stymied. This paper demonstrates how to use SAS Business Intelligence and PROC OLAP to create two challenging cubes: a per member per month cube from insurance claim data and a quality of care cube from patient chart data |
vital signs, proc step, sas enterprise guide, raw data, business intelligence, insurance claims, proc sql | source | 10 |
| 127 | Hear the Competition Running, See the Opportunities Ahead, and Run Faster: How Aeroplan Canada Turned a Threat into a Winning Proposition | Michel Girard | Competition never sleeps in the loyalty industry as coalition, frequent flyer, and frequent guest programs are all fighting for their share of customer loyalty. Anticipating the launch of a competitive loyalty program, Aeroplan Canada turned what many thought to be a threat to their leadership into an opportunity to reinforce their position in their market. Leveraging analytics on member behavior and attitudes, Aeroplan Canada market initiatives reassert the leadership position as a key partner in the process | data warehouse, response variable | source | 5 |
| 128 | HELP, My SAS Program isn't Working: Where to Turn When You Need Help | Kimberly J. LeBouton | Instead of a quick code fix, I often assist the SAS user by troubleshooting their issue with my knowledge of SAS and the SAS community. For over 20 years, I have provided SAS Technical Support, and this paper will present strategies I have used to work through simple to complex technical problems, and will include access to my "cheat sheets" | put statement, sample, sas log, macro put | source | 4 |
| 129 | Highlighting Unusual Behavior in Insurance Based on Social Network Analysis | Carlos Andre R. Pinheiro | Insurance companies have evolved into a highly competitive market that demands companies to put in place an effective customer behavior monitoring system, both in terms of usage and value. Unusual behavior in insurance can represent distinct business meanings, such as heavy users or suspicious transactions. Social network analysis can be used to raise business knowledge in relation to the average customer's behavior and, therefore, to highlight unexpected usage. Regardless of the reason for high usage in relation to insurance services, high usage substantially impacts the operational cost, either by the claims' expenses or the possible fraud events. SNA can be used to evaluate the different roles assigned to the actors and their relationships, allowing companies to deploy a more effective program to better understand and monitor the claims' transactions | outlier, social network analysis, proc princomp, auto insurance, cluster analysis, link analysis, univariate analysis, insurer, proc freq | source | 9 |
| 130 | HIPAA Compliance: Meeting Privacy Requirements in Combining Multiple Data Sets Using SAS Functions | Siew Ching Ang, Paul M. Darden |
Research in healthcare often involves identified data. These datasets contain protected health information (PHI) that has to be removed to meet use and disclosure requirements for research under HIPAA. There are varying databases in which patient information is stored. These can be multiple datasets on the same patients or different patients that need to be combined for analysis. When combining multiple datasets for further analysis, it becomes a challenge in aligning these datasets, and at the same time, protecting PHI. This paper highlights the importance of examining the format of each dataset, and discusses steps to prepare the datasets before removing the PHI. Subsequently, we introduce, in progressively complex methods, combinations of SAS functions, such as DO-LOOPs, SEED, RANUNI, NODUPKEY, EOF (end of file) functions, to replace the PPI with research appropriate identifiers, specifically variables embedded in medical records and administrative datasets | phi, unique key | source | 12 |
| 131 | How to Build ADaM Basic Data Structure from Mock Up Table | Kevin Lee | The paper is intended for Clinical Trial SAS programmers who are interested in ADaM(Analysis Data Model) BDS(Basic Data Structure). The paper will discuss basic structures of ADaM data sets. It will also discuss how ADaM data sets are built from Mock Up tables. The paper will provide examples of Mock Up tables, ADaM data sets and SAS codes that generate final numbers of Mock Up tables from ADaM data sets | sas programmer, proc sql, proc step, sample, cdisc | source | 4 |
| 132 | How to Customize Your Data Analysis with SAS OLAP Cube Calculations | Tatyana Petrova | When a cube designer is working on an OLAP cube structure, several decisions should be made: Cube granularity Content and structure of dimensions What data facts from input tables are relevant to your analysis and what calculations do you need to apply to this data. In this paper, we focus on the last decision. SAS OLAP Server provides various means to customize analysis of input data facts. Calculations can be created via: Cube Measures Cube-defined Calculated Measures and Members Global-scope/Session-scope Calculated Measures and Members * Query-scope Calculated Measures and Members. For each of these levels of definitions, various functions and statistics can be applied. This paper is targeted toward OLAP cube designers and advanced OLAP cube users. It discusses how to design OLAP cube calculations, where to define them, what functions are available for your use, and how to embed more control into a flow of your MDX programs | sas enterprise guide, sas function, proc step, sas base, sum, time series analysis | source | 22 |
| 133 | How to Drive Your SAS Programs on Cruise Control | Teo Gamishev | This paper introduces a simple, robust and efficient system to automate almost any SAS program or project, requiring submitting series of SAS programs. The reader will be exposed to the system's big picture and technical details on how to build and apply it. When created and applied on the first project, the new system provided the following results: Programmer's time: reduced from 13 hours to 13 minutes. That is 60 times or 6,000% improvement! Programmer's efforts: from 9 to 3, on a scale from zero to 10. Three times or 300% improvement! Programmer's errors: reduced to zero. Chances for human errors are eliminated! Programmer's experience: from stressful to joyful. Immeasurable improvement in the quality of work and life! The system continues to provide similar results on all of its subsequent applications to different complex projects and single programs | macro variable, quality control | source | 6 |
| 134 | How to Manage E-mails from SAS | Teo Gamishev | Setting up part of your email workload on autopilot from within SAS can eliminate the need of redundant work and significantly increase your job efficiency when dealing with repetitive tasks. SAS has built in emails generating engine, which when tuned properly, works very well with every email provider. Here you'll find an example of fully automated process of emailing reports to several customers, using SAS | macro variable, options statement | source | 5 |
| 135 | How TV 2 Improves Data Quality and Facilitates a Unified Customer View for Optimizing the Customer Experience | Svein O. Gundhus, Terje Vatle |
Norway's largest commercial television broadcaster, TV 2, wanted to improve the customer experience by better targeting customer interests in their one-to-one campaigns. They were challenged with having multiple data sources with different customer definitions, and they were not able to get a unified customer view showing interests, usage patterns, and purchases for each customer. In this presentation, TV 2 will share how the use of DataFlux and SAS have given them a single customer view that they can use for analyzing customer behavior to better understand customer needs. In addition, they will share how this knowledge will be used for targeted one-to-one campaigns and share thoughts on some of the legal and business ethical considerations they are facing | customer experience, data validation rules, sas data integration studio, data standard, data cleaning | source | 12 |
| 136 | How You Can Identify Influencers in SAS Social Media Analysis (And Why It Matters) | Barry deVille, Don Hatcher, Gurpreet S. Bawa |
There are many ways to calculate influence in social media. We use an approach that distinguishes between content generators and conversation generators. This allows us to distinguish between the generation of potentially passive content versus the generation of conversations that include content with "pass along" appeal. The "pass along" content is potentially more influential since it exposes the content to more actors and embodies a social "referral" component which has been demonstrated to affect the information and behavioral impact of messages. Content Generator Metric. In its simplest form, this is the number of communications over a given time period. A social media user who posts comments to a huge follower community generates a large content influence score. Unless the information is re-tweeted or mentioned in someone else's post, the "conversation" part of the influence score will be relatively low. Conversation Generator Metric | network analytics, social network analysis | source | 8 |
| 137 | If Its 18722 Then This Must Be SAS Global Forum: An Introduction to SAS Dates | Peter W. Eberhardt | For many new SAS programmers, the use of SAS date and DATETIME variables is often very confusing. This paper addresses the problems that most programmers have. It starts by looking at the underlying data representation of date and DATETIME variables, and then discusses how to change this data representation into a visual representation through the use of SAS formats. From there, the paper discusses date arithmetic by first demonstrating the use of simple arithmetic to increment dates, and then moving on to SAS functions which manipulate SAS date and DATETIME variables. This paper is introductory and is intended for new SAS programmers |
sysdate9, date variable, datetime variable, time variable, macro variable, datetime, sas function, sas programmer, time, proc sql, character variable, relational database, format statement | source | 13 |
| 138 | If You Can't Learn It from a Book, Why Are You Reading This? | Stephen M. Noga | People management, an inexact science if ever there was one, is often thrust upon individuals as they move up the corporate ladder. With promotion comes added responsibility whether a person is ready for it or not. How is a person supposed to learn to manage other people? Seminars, and online courses, and books, Oh My! The choices are numerous and chances are that knowledge will be obtained. However, how did you learn to deal with missing data where none was expected? You experienced the frustration after your program produced erroneous results and you learned how to prepare for this situation the next time. Management is no different. Welcome to my observations from twenty-one years of managing SAS programmers | sas programmer, statistical programmer | source | 6 |
| 139 | Impact Analysis - The Smart Way | George G. Mendoza | Very often, Business Analysts and SAS programmers need to analyze the impacts of making changes to existing tables, datasets, macro variables etc. In order to fully understand the im pact of the changes it is crit ical to list all SAS codes that use or reference the objects (tables, datasets, macro variables etc.) that are changing. This paper describes an innovative method for listing all the impacted code | macro variable, dataset, proc sql, proc step | source | 6 |
| 140 | Impact of Regulations on Risk and Trading Analytics in Capital Markets | Jeff Hong | New and pending regulations in the banking industry will no doubt have an impact on current trading practices and business relationships for capital markets firms. In particular, regulations will increase the frequency and complexity of risk analytics. This paper examines the current and future challenges surrounding risk management and analytics requirements, and the technologies that can be deployed to address them. The results of a Wall Street and Technology (WS&T) survey - jointly sponsored by SAS Institute and Platform Computing - of over 200 large and small Wall Street firms on their preparedness to respond to additional risk requirements will also be discussed. | basel iii, capital adequacy, liquidity risk, big data, market risk, monte carlo, counterparty risk, credit risk, stress testing, sas risk engine, data mining, correlation | source | 13 |
| 141 | Implementing a Data Mining Solution with SAS and Teradata | Angela Watson | This paper discusses the challenges that were overcome, and the benefits that were achieved by implementing SAS Analytics with Teradata at Overstock.com. Follow the steps from "Decision to Implement" to "Production Analytics," as the Overstock.com team and SAS analytical consultants worked side by side. Experience business transformation, from initial model development and knowledge transfer to business process change, that resulted in significant performance and productivity improvements | data mining, teradata | source | 3 |
| 142 | Implementing SAS Grid Computing in an Analytical Environment | Shivashanker Bitla, Richard Kerwin |
This paper focuses on the value of implementing SAS Grid in a large analytical environment and uncovers the lessons learned in the course of the Grid implementation and subsequent rollout. The former environment consisted of 4 SAS v9.1.3 EBI installations on four server partitions , which served as Adhoc, De velopment, Production and Metadata environments, and were housed on one physical ly large server. The new SAS Grid is an internally hosted SAS 9.2 M 0 EBI installations housed on a SAS Grid with 8 identical server nodes using a shared file system. Primary objectives for converting the SAS environment to a Grid with shared file system were to provide a cost effective, readily scalable in-house analytical environment for a rapidly expanding SAS User community that would replace an expensive, externally house d, difficult to upgrade environment that lacked the ability to distribute data across a shared file system | sas grid, grid computing | source | 7 |
| 143 | Improving Customer Loyalty Program through Text Mining of Customers Comments | Maheshwar R. Nareddy, Goutam Chakraborty |
Typical surveys contain closed-end questions that generate structured numerical data and open-ended questions that generate unstructured textual data. Given the perceived difficulties in analyzing texts, most companies often ignore textual data or simply look at summaries of comments. We illustrate how textual data can be grouped together to generate insights into customers' expectations and how such groupings can be used as input variables to build better predictive models than models based on numerical data alone. Data was collected at a national conference via a survey that had numerical and four open-ended questions. Ten clusters were found in grouping of textual comments. The target is a binary (Yes/No) variable about best loyalty program in the industry. Data was split into training and validation before building predictive models. The best predictive model using numerical data has a misclassification rate of only 26.5% and a sensitivity of 60% in the validation sample | data cleaning, feature selection, validation set, response variable, regression analysis | source | 8 |
| 144 | Improving Performance: Using SAS Merchandise Planning Release 6.1.3 to Get the Most! Gain the Value! | Tad Bickford, Donna Mazzone |
Are you tired of waiting for data to refresh? If performance is becoming more of a challenge in your organization, then look at your current SAS Merchandise Planning deployment and leverage the benefits of release 6.1.3. This SAS Merchandise Planning release offers many benefits over previous releases, including improvements to recalculations, which enhance performance when you open a worksheet. Thus, your organization can be more efficient during the planning process. Through the latest case studies, this paper demonstrates the value that is delivered from the simplified metadata calculations and faster worksheet performance and helps you visualize how your organization can achieve improved productivity. Basic metrics are used to show how you can get the most from the performance improvements and gain the value by leveraging the benefits of release 6.1.3 (and maintenance releases) in your organization | excel, sas base | source | 11 |
| 145 | Improving Searchable Access to SAS Global Forum Conference Papers Using Semantic Analysis Methods and Text Analytic Technologies | Richard W. La Valley, Denise A. Bedford |
SAS Global Forum has offered attendees the ability to search the contents of the papers being presented prior to the conference. This searchable access has parameters: title, author, abstract, keywords, and the conference section. Current semantic analysis technologies make it possible to leverage the natural language processing methods, organizational and domain knowledge, and the full text itself to achieve significant productivity gains -in extending a ccess points for conference papers, generating more granular access to information, producing more objective descriptions and categorization of content. This paper presents experimental results on some of those gains | goodness of fit, proc freq, sample | source | 16 |
| 146 | In Control of Your Data Warehouse Processes With the Help of SAS Stored Processes and the SAS Information Delivery Portal | Ralf Vaessen, Danny Pannemans, Juan M. Quesada, Koen Vyverman |
In the current economic climate, external and internal pressure on the quality of information management processes is high and still on the rise. In the industry of asset management for pension funds in particular, external regulators require detailed insight into exposure and risk. Keywords are control, transparency, auditability, evidence, data- quality, time-to-market, and security. In this paper, we describe a set of SAS tools for the operational management of data integration and reporting processes at APG (one of the world's leading pension fund management companies): real-time monitoring of data integration jobs; generation and publication of multi-client reports; data quality controls for completeness, consistency and accuracy; and the monitoring of report consumption. These tools are serviced via SAS Stored Processes, executed from within the SAS Information Delivery Portal, and used by members of the APG data warehouse team | sample, macro variable, data cleaning, quality control, sas log, data step, data warehouse, data integration, proc freq, output delivery system, excel | source | 24 |
| 147 | Increasing Efficiency in Optimization | Robbie Reynolds, Angela Williams |
In marketing optimization, the number of rows in the communications table correlates to the size of the mathematical problem that the optimization algorithm attempts to solve. If you can reduce the number of rows, the "optimization problem" will be solved more quickly and efficiently. At Transamerica Life & Protection, one of our programs set up for optimization using a standard methodology, required over 700 rows on the communication table. Due to size, it required resources beyond what our servers could consistently support. Through some creative brainstorming, we were able to significantly reduce the size of the "optimization problem" and complete the project | data validation rules, response variable | source | 5 |
| 148 | Innovations in Data Management - Introduction to Data Management Platform | Wilbram Hazejager, Pat M. Herbert |
DataFlux, a SAS company, recently delivered the first installment of the DataFlux Data Management Platform in order to provide complete access to enterprise data. This set of products, which will be included with many SAS 9.3 product offerings, covers the gamut of data management needs including data quality, data integration, and master data management. In this presentation, we provide a view of the fundamental product capabilities delivered with the DataFlux Data Management Platform and show how a targeted user interface can make even the most challenging problems simple to solve | data integration, data validation rules, data governance, data cleaning, master data management, data profiling | source | 8 |
| 149 | Integrating an Apple iOS Application with SAS Platform Services | Zachary V. Marshall | Apple's iOS operating system for mobiles is available on over 120 million devices and is increasingly being used as a platform for in-house enterprise and business applications. iOS offers an unmatched user experience among handhelds, and is spurring an ever-growing enterprise adoption rate. This paper details the process of creating an iOS application that integrates with the SAS Analytics Platform. Principally, it will examine the interaction between an iOS application and SAS BI Web Services, specifically leveraging the new transport types in SAS BI Web Services 9.3, but also offering tips and tricks for integrating with other SAS SOAP Services | web service, sas management console, macro variable, sas enterprise guide, sample | source | 19 |
| 150 | Integrating SAS and ArcMap: A Case Study of European Internet Use and Population | Hillary B. Huffer | This paper demonstrates how to effectively navigate between the SAS and ArcMap environments. European internet use is utilized as an example to illustrate various techniques, including: how to use a variety of data formats, how to import shapefile data to SAS, and how to export SAS output to an ArcMap map layout. This paper concludes that, as expected, European internet use and population are positively correlated and that SAS and ArcMap can work together seamlessly | txt, correlation, scatter plot, sas enterprise guide | source | 4 |
| 151 | Integrating SAS Information Delivery Portal with your Organization's Brand | Paresh M. Patel | Corporate intranets have grown in size and complexity and have presented organizations with content management challenges; as available information has grown, users have demanded personalization and customization. In response, Webmasters have created portals to help users manage their data, applications and information. An Enterprise Portal is designed to share information and promote collaboration across multiple workplaces. Integrating the appearance of SAS Information Delivery Portal with your organization's brand is important to the acceptance of the new tools and information it provides. This is achieved by creating a custom theme that implements some of the following changes: ¾ Adding company logo and banner. Changing banner utility bar to transparent. ¾ Changing default preferences to vertical navigat ion, locale and other aspects of the Portal | sas management console, access control | source | 12 |
| 152 | Introducing SAS Structural Equation Modeling: A New User Interface That Brings the Power of SAS/STAT Software to JMP Software | Wayne E. Watson | Structural equation modeling evaluates how well a theory in the form of a model fits data. Models might involve a number of interrelated variables and include latent variables, which cannot be measured directly, along with other variables that are directly measured. A new application, SAS Structural Equation Modeling for JMP 1.1, features a graphical model builder that is useful for creating these interrelated models. The user interface appears in JMP software, and the calculations are performed by the SAS/STAT CALIS procedure. You create models by dragging variables onto a diagram, perform the analysis, and save and reuse the results |
proc calis, covariance, dataset, correlation, latent variable, raw data, proc freq | source | 18 |
| 153 | Introduction to ODS Graphics for the Non-Statistician | Mike Kalt, Cynthia L. Zender |
Are you a History, English, or other humanities major who has stumbled into SAS programming? Are you a business analyst or report analyst whose statistical knowledge ends with mean, median, percentiles, and standard deviation? Don't know a fitted loess curve from a survival estimate? Need to produce some series plots and bar charts and maybe the occasional box plot? Don't panic! This presentation is for you! This presentation illustrates how to use Base SAS procedures and new statistical graphics (SG) procedures (in particular, SGPLOT and SGPANEL) in SAS/GRAPH to produce simple plots and bar charts. Once you know the basics of the SGPLOT statements to produce single graphs, learning SGPANEL to create paneled output will be a cinch. Through concrete examples, this paper will guide you through the basics of producing and customizing simple graphs using the new SG procedures. In addition, use of the ODS GRAPHICS statement for setting graph options is covered |
proc step, bar chart, proc sgplot, series plot, histogram, proc freq, proc sgpanel, proc sgscatter, vertical bar chart, sas base, nominal variable, proc gchart, proc univariate, scatter plot, line chart | source | 28 |
| 154 | Investigating Web Search Engine Retrieval of Medical Information on Antidepressant Discontinuation Syndrome Using SAS Text Miner | Barry W. Row | Availability of information on medical topics is increasingly important for patients to be able to make informed choices regarding their own healthcare. Given that Web search engines are now a primary information resource, the present study used SAS Text Miner and the SAS %tmfilter Web crawling macro to retrieve and analyze documents from the Web search engines Bing, Ask.com, and Yahoo related to antidepressant discontinuation syndrome, a common but little-known side-effect of drugs used to treat depression. Cluster- and concept-link analyses revealed that distinct differences were found between search engines, with the documents retrieved from the Bing search engine related to specific drug treatments, while the documents retrieved from the Ask.com and Yahoo searches were related to more general information on antidepressant discontinuation syndrome | link analysis, default parameter | source | 9 |
| 155 | JMP 9 Add-Ins: Taking Visualization of SAS Data to New Heights | Eric Hill | For several years, JMP has been a great tool to use for discovering relationships in your SAS data. JMP 9, released last fall, vastly extends the analytic and discovery capabilities that were previously available, including: many improvements to the interactive graphical discovery tool, Graph Builder the ability to automatically place maps behind geographical data * the ability to reach out to R to execute analytical methods Combine those improvements with the ability to create add-ins for JMP that can be deployed easily throughout the enterprise and you have a tool that can take statistical discovery and information visualization to new heights in your organization | proc sql, proc step, regression analysis, proc export, dataset, heatmap, estimate, sas enterprise guide | source | 16 |
| 156 | JMP 9 and Interactive Statistical Discovery | John P. Sall | JMP 9 represents a major new revision of statistical discovery software from SAS for engineers, scientists, and business analyst s | data cleaning, excel, proc hpsplit, main effect, correlation, dataset, proc freq | source | 15 |
| 157 | JMP as an Analytic Hub: Using JMP to Build Custom Applications via SAS and R Integration | Kelci J. Miclaus | In today's data-rich era, statisticians and analysts have a number of tools available for data analysis. This paper presents examples and explores applications of using JMP 9 as a data analysis hub. The integration of JMP with both SAS and R-coupled with a new add-in architecture that makes it easy to create, distribute, and install custom applications-provides unlimited options for data exploration and analysis. Examples presented highlight the incorporation of R packages to perform novel analysis techniques as well as SAS procedures to implement advanced statistical methods within the JMP graphical user interface. Such applications allow you to take advantage of the dynamically interactive JMP visualizations, the depth of SAS statistical procedures, and the breadth of R packages available. Addressing these examples, tips, and techniques on building custom dialog-based applications via JMP Scripting Language (JSL) with SAS and R programming is highlighted, as well as how to create a JMP add-in. | source | 13 | |
| 158 | LAG Function Combined with Conditional Functions: Useful for Identifying Differences in Like Data | Andrew Hummel | The LAG function is useful in identifying subtle differences in rows with similar data. This is especially valuable when the data set contains a large number of rows. Additionally, when conditional functions are used in conjunction with the LAG function specific limits can be used to flag only particular differences between rows | lag function, macro loop, proc sort | source | 8 |
| 159 | Lead Nurturing Using SAS Customer Intelligence Solutions | Matt Fulk | Discover how SAS uses SAS Customer Intelligence solutions to score, nurture, and manage a high volume of Web leads. This paper covers the business objectives, execution, and results of active nurturing campaigns at SAS. Learn how SAS implemented a lead nurturing strategy that tracks and acts upon online behavior, automates communications to prospects, scores Web leads, and improves lead-to-open-sales conversion rates using by SAS Customer Intelligence solutions, including SAS Marketing Automation and SAS Digital Marketing | next best offer, key performance indicator | source | 7 |
| 160 | Learn SAS in 50 Minutes | Subhashree Singh, Sovraj Singh |
SAS is the leading business analytics software used in a variety of business domains such as Insurance, HealthCare, Pharmacy, Telecom etc. Very often, business analysts and other professionals with little or no pro- gramming experience are required to learn SAS. This paper will explain the key concepts and the minimum know- ledge required to get started with SAS. The information presented in this paper will enable new SAS users to get up and running in a very short amount of time | dataset, proc step, missing value, sysdate9, date variable, proc contents, proc freq | source | 13 |
| 161 | Lets Get Connected: How We Link to our Data | Jenine Milum | You've got data, so how do you get it into SAS? One of the great strengths of SAS is that it is 'data agnostic' and can accurately read and write any of a dozen data storage formats. An additional benefit within SAS is that for any given format there is more than one method or technique to access it. How do you refer to them? Which operating systems are used? Which methods are most appropriate in which situations? This presentation provides, at a high level, a quick reference to the many options, tools, and methods that connect us with our data | dataset, catalog, cards, raw data, filename statement, sas base, catalog entry, proc sql, data step, excel, proc step | source | 10 |
| 162 | Lets Give Em Something to TOC About: Transforming Your.pdf Files Table of Contents | Bari Lawhorn | In PDF files, the table of contents provides a map that helps your audience to navigate the document easily. However, the default table of contents that is generated by the SAS Output Delivery System (ODS) destination, while informative, is fairly utilitarian. Your procedures and DATA steps generate tables and graphs that have meaning to you and your audience. Likewise, the table of contents should also be as meaningful as possible by clarifying the contents of your PDF. This paper explains and demonstrates step by step how to use the following statement, options, and procedures to customize your table of contents: the ODS PROCLABEL statement the CONTENTS= and the DESCRIPTION= options the DOCUMENT destination and procedure the TEMPLATE procedure These tools provide you with the flexibility and the power to customize your table of contents so that you really leave your audience with something to TOC about! |
proc step, proc report, sample, proc gchart, output delivery system, ods pdf, proc greplay, proc freq, proc print, proc reg, proc sql, missing value, proc gplot, sas programmer, proc sgpanel, proc tabulate, macro variable, proc template, file statement, sas output | source | 18 |
| 163 | Linear Optimization in SAS/OR Software: Migrating to the OPTMODEL Procedure | Robert C. Pratt, Edward P. Hughes |
PROC OPTMODEL, the flagship SAS/OR optimization procedure, is intended to supersede the INTPOINT, LP , and NETFLOW procedures for linear optimization. PROC OPTMODEL 's rich and flexible syntax enables natural and com- pact algebraic formulations of optimization problems. And the linear programming, mixed-integer linear programming, and network solvers available in PROC OPTMODEL are much faster than those in the INTPOINT, LP , and NETFLOW procedures. Beyond the easier modeling and access to improved algorithms, PROC OPTMODEL also provides programming ca- pabilities that enable you to develop customized solution methods. This paper uses several examples to illustrate how to migrate to PROC OPTMODEL |
proc step, odds ratio, linear programming, proc optlp, proc optmilp | source | 21 |
| 164 | Living with Generalized Linear Mixed Models | Walter W. Stroup | In the 1980s, before PROC MIXED or PROC GENMOD, "linear models" meant the "general" linear model as implemented by PROC GLM. Three decades later, the meaning of "linear models" has fundamentally changed. The introduction of PROC GLIMMIX in 2005 was a watershed moment. Now "linear model" means "generalized linear mixed model." The notion that the "general" linear model was once considered "general" seems quaint. In living with PROC GLIMMIX over the past five-plus years, several issues have become apparent that were not issues during PROC GLM's heyday. One set of issues concerns inference space and conditional versus marginal modeling. When you learn about the GLMMs, you often find that you must first "unlearn" the old ways of thinking about models. The second set of issues concerns power, sample size, and planning. These areas have not caught up with GLMMs |
proc step, binomial distribution, sample, sample size, anova, poisson distribution, power analysis, proc genmod, proc glimmix, proc glm, likelihood function, proc mixed, proc power, type i error, degrees of freedom, significance level, treatment effect, normal distribution, covariance | source | 18 |
| 165 | Logit Models in Practice: B, C, E, G, M, N, O, ... | Joseph C. Gardiner, Zhehui Luo |
Logit models appear in a variety of forms in applications in biostatistics, epidemiology, economics, marketing research and sociology. They are used to model the relationship between covariates and various types of discrete outcomes from the ubiquitous binary logit model for a two-level response to the conditional logit and multinomial (generalized) logit models concerning polytomous responses. Covariates may vary by characteristics of both the individual and response. For example, when assessing a consumer's choice of health insurance plan or health care provider, or selection of a treatment regime (surgery, medical management, or no treatment), the probability of choice depends on the consumer's own circumstances, utilities and preferences. Nested logit models allow for modeling the sequence of the decision process faced by the consumer by grouping alternatives at each stage into nests | logistic regression, likelihood function, proc step, logistic distribution, conditional logistic regression, latent variable, confidence interval, regression analysis, sample, output, proc logistic, probability distribution, odds ratio, response variable, proc genmod, maximum likelihood estimation | source | 20 |
| 166 | Longitudinal Data Techniques: Looking Across Observations | Ronald P. Cody | One of the most difficult tasks for a SAS programmer is to perform operations across multiple observations. For example, you may have a data set of patient visits, with a variable number of visits per patient and the data for each visits stored in a separate observation. Some of the techniques for working with such data include the RETAIN statement, FIRST. and LAST. logical variables, the LAG function, and the use of multiple SET statements (each with a separate FIRSTOBS= option). Other techniques for summarizing data for each patient (such as the number of visits, the mean and median of some variables) involve procedures such as PROC FREQ and PROC MEANS to output summary data sets. We will demonstrate and discuss these techniques in this tutorial. |
proc step, proc freq, lag function, by group processing, set statement, longitudinal data, retain, proc means | source | 7 |
| 167 | Macro Design Automation Tool (MDAT): A Methodology for Storing Macro Design Information, Generating Documentation and Code Templates, and Automating Parameter Validation | Richard L. Schneck, Mindy Rodgers |
As members of a SAS macro development team, we realized that we were spending considerable resources dealing with the basic tasks involved with the documentation and testing of each macro we develop, particularly with respect to information about the macro's parameters and source data requirements. If we could devise a systematic method of capturing this information, then it would be possible to develop a process to automate the creation of user documentation, parameter-validation code, testing documents, and even a customized template for the macro. Besides saving time and effort, major benefits of such a process would be the standardization and improved quality that would naturally result | data cleaning, stored macro, macro call, raw data, missing value, macro parameter, excel, sample | source | 12 |
| 168 | Macros and Conventional Macro Variables: Effective Tools for Customizing Tabular Output in SAS ODS | Perry Watts | While it is understood that macro variables are referenced in ODS by using MVAR and NMVAR statements, macro programs continue to play a vital role in ODS. To back up this claim, the TABLETEMPLATE macro that generates a customized table in ODS is reviewed in depth. Looping in the macro eliminates the need for hard coding, and multiple ampersand macro variables (&&f&i) provide the only viable means for inserting data dependent text strings into a table. Pre and Post macro versions of the code are presented to emphasize the role played by macros in ODS |
macro variable, proc step, proc template, proc sql | source | 6 |
| 169 | Macros for Managing Messy Data: Handling Duplicate Study Participants, and Making Fuzzy Matches Across Multiple Data Sets | Jefferson L. Wood, Dennis G. Fisher, Lucy Napper, Grace L. Reynolds |
The CSULB, Center for Behavioral Research & Services (CBRS) conducts behavioral research among hard-to -reach populations in Southern California. During data collection, CBRS staff members attempt to identify each research participant and link the participant to a unified participant record which details all prior contact with the agency. However, incomplete or inconsistent personal information can cause staff members to establish a new ID rather than amend an existing record. These errors are often identifi ed after substantial research data has been collected. CBRS uses a SAS macro and hash object to manage participants with multiple IDs without revising raw data sets or requiring analysts to know anything about the IDs established for an individual. Changes can be rolled-back in the rare case of a misidentified duplicate (e.g., twins or a Sr./Jr. relationship misidentified as the same individual) | date variable, hash table, proc step, output | source | 10 |
| 170 | Maintenance for the Minimalist A SAS Administrators Guide to Supporting a Windows SAS Metadata Environment | Laura G. Liotus | Upon maintaining a SAS environment, there are a few essential tasks an administrator should perform. Depending on the environment, for compliance reasons, an audit o f login activity might need to be retained. In the event of a disaster, a hardware failure or something worse, a backup of the SAS metadata is needed for recovery p urposes. For a quick restore of individual objects and for migra tion purposes, the export program includes a proces s to export all objects with one script. Base SAS 9.2, SAS Managem ent Console 9.2 and SAS Data Integration Studio 4. 2 provide new opportunities that allow for these thre e items to be maintained. The SAS administrator can combine these new approaches with some qualities of the pre vious version's methodologies. Combining the new an d old techniques, a complete, minimalistic approach to ma intenance that has been set-up at Community Care Be havioral Health is illustrated | sas management console, business intelligence, dataset, sample, sas administrator, batch job, excel, audit trail | source | 10 |
| 171 | Making Cube Drill-Through-to-Detail More Detailed | Gloria M. Faley | SAS Web Report Studio has long offered the ability to view the specific observations from the underlying data that make up an aggregate value in the cube. However, you can use the functionality of WRS to look at that same detail data that is associated with an OLAP cube by using report linking. This paper describes how you can set up a parameterized report using the SAS Web Report Studio prompting framework to link OLAP data to its source data. The paper shows how to set up a report that drills into a specific level on a dimension in an OLAP report. From that level, you can immediately go to the aggregated data as well as apply totaling and formatting to the flattened detail report | dataset, character variable | source | 12 |
| 172 | Making SAS Enterprise Guide Easier to Use | Tom Kari | Although SAS Enterprise Guide is a wonderful tool for data analysis, the design and format of the input data can have a major influence on the efficiency and accuracy of the analytical results. Fortunately, a number of techniques exist both within the SAS environment and in external data management products to improve the usability of these data. This paper explores the use of views, naming standards, and application of SAS labels and formats to external database tables to demonstrate how typical survey data can be made easy to understand for any level of user | sas enterprise guide, dataset, data science | source | 9 |
| 173 | Making SAS Training Stick | Christopher J. Bost | Training is required to develop SAS programmers. The knowledge and skills taught in class, however, are not always retained by students. This paper describes a five -step process for designing and delivering memorable SAS training: 1) assessing needs; 2) preparing for training; 3) delivering training; 4) transferring training; and 5) evaluating results. Best practices are identified and practical tips are supplied to help implement each step. This process helps to improve retention and to maximize the return on your SAS training investment | macro definition, macro facility | source | 10 |
| 174 | Making Use of Incomplete Observations in the Analysis of Structural Equation Models: The CALIS Procedures Full Information Maximum Likelihood Method in SAS/STAT 9.3 | Yiu-Fai Yung, Wei Zhang |
Data are precious. Missing values are unavoidable. Instead of throwing away incomplete observations or imputing missing values ad hoc, what would be a better way to analyze data with random missing values? The full information maximum likelihood (FIML) method of the CALIS procedure in SAS/STAT 9.22 and later enables you to use all the available information in your data to estimate your structural equation models. This paper illustrates how you can use PROC CALIS to deal with random missing values in the following data-analytic situations: (1) estimating means and covariances, (2) regression analysis, and (3) structural equation or path modeling. Other methods for treating incomplete observations are described in a systematic manner. The FIML method is supe- rior to the ad hoc methods for analysis with incomplete observations |
proc calis, proc step, imputation, sample, covariance, missing value, regression analysis, mean imputation, likelihood function, multiple imputation, estimate, maximum likelihood estimation, proc reg, standard deviation, model training, data science, by group processing | source | 20 |
| 175 | Managing your SAS Assets | Stephanie R. Thompson | Hiring talented SAS professionals can be an expensive proposition. While it is great to get another position for your department, replacing someone can be a real downer. Why did they leave? Was it the work? Did we make a poor hiring decision? Was it just a better opportunity? Could they have been happy here? Maybe they just did not like me. All of these questions are valid, and you can keep guessing and never really know. As they say, an ounce of prevention is worth a pound of cure. What can you do as a manager to keep your best talent happy and challenged? Keeping those in your department engaged is a big piece of the puzzle. Not a manager? Well, you're not off the hook either. There are things you can do to help your manage you better. I've been on both sides of this fence, and this paper will talk about some of the things that will help keep everyone happy. Think of your company's SAS talent as SAS assets! | job security, full join | source | 5 |
| 176 | Marketing Investment Optimization Framework for AmEx Travelers Cheques, Clustering Consumer Behavior Leveraging SAS/ETS to Uncover Investment Opportunities in an Environment Demanding Quick Decisions | Landon Starr, Pieter van Ispelen |
Creating a standard, consistent, and effective method of measuring marketing investment effectiveness can be very challenging. Adding a global investment environment, with dramatically varying global consumption patterns, can compound those difficulties. Throw in macro-economic impacts, and the desire to determine the best investment amounts to be allocated to marketing a product, and these challenges manifest themselves into the journey of American Express' Travelers Cheque marketing investment analytics work over the past few years. The purpose of this paper is to share a versatile marketing investment optimization framework, focusing on clustering consumer behavior, leveraging SAS/ETS software for time series sales pattern trends, and utilizing a Monte Carlo type of |
sample, monte carlo, confidence interval, proc step, proc fastclus, outlier | source | 11 |
| 177 | Master Data Management, the Third Leg of the Data Management Stool: a.k.a. the DataFlux qMDM Solution | Pat M. Herbert, Wilbram Hazejager |
Master data management has become one of the fastest-growing application areas in recent times because of the need for organizations to leverage the valuable assets hidden within disparate enterprise data stores. DataFlux, a SAS company, has created the DataFlux qMDM Solution in order to allow you to connect the dots between these previously disconnected systems without needing to make changes that can negatively impact operational systems. This paper provides an overview of the qMDM Solution and provides real-world examples of how one can achieve the goal of mastering data to connect the information across multiple disparate systems... and the techniques for accomplishing that task are changing. In this presentation, you see how the DataFlux qMDM Solution can put you on the path to developing a complete view of your data | data integration, data cleaning, data governance, master data management, data validation rules | source | 9 |
| 178 | Matching-Adjusted Indirect Comparison Analysis Using Common SAS 9.2 Procedures | Elisabetta Malangone, Steven Sherman |
This paper presents a novel matching-adjusted approach to indirectly compare survival estimates for competitive treatment options. Using patient-level data for the treatment arm and summary patient characteristics and survival outcomes for the comparator, matching variables prognostic for survival are chosen. A program involving an extension of a common SAS 9.2 procedure, PROC SURVEYSELECT, is applied to select 1000 random repeated sub-samples from the original population with the same distribution of matched variables. Using Output Delivery System (ODS) and survival analysis procedures, the survival estimates are computed for each bootstrapped sample and a 95% confidence interval (CI) is inferred around the mean of the sampled survival estimates. These outcome measures may then be compared to those reported for the comparator treatment |
sample, hazard ratio, subject-level data, median survival, confidence interval, proc calis, inferential statistics, proc step | source | 6 |
| 179 | Maximize your Investment: Building a Portal to Your SAS Resources | Stacy Hobson | Most SAS users are unaware of the wealth of resources that are available to them. These include a variety of sources, from internal to their own organization to SAS itself. Help your users get the most out of the investment that has been made by giving them quick, relevant, and intuitive access to these resources. This paper shows you simple yet powerful ways to connect SAS users to the resources that will help them get their jobs done and maybe even accomplish things they did not think possible. We will explore traditional methodologies, as well as social media techniques to make these connections | sas base, account | source | 5 |
| 180 | MCPOWER: a Flexible Macro Suite for Generating Monte Carlo Power Estimates for Linear, Logistic, and Poisson Regression Models | Ken Kleinman | I describe and demonstrate a macro suite which estimates power for linear, logistic, and Poisson regression using Monte Carlo methods. In this approach one repeatedly simulates data sets under the alternative and estimates the power as the proportion of data sets in which the null can be rejected. The macro allows a wide variety of predictor distributions and produces raw estimates as well as smoothed estimates which draw strength across the assessed sample sizes. It optionally generates plots and printouts including an interpolated estimate of the number of subjects necessary to achieve a target power | regression analysis, poisson regression, poisson distribution, logistic distribution, proc step, null hypothesis, logistic regression, proc power, monte carlo, sample size, sample, proc reg, proc glmpower, relative risk, odds ratio, rand | source | 5 |
| 181 | Measures, Members, and Sets, Oh My! Advanced OLAP Techniques | Stephen M. Overton, Bryan Stines |
Going beyond basic OLAP measures, members and sets gives the analytical user more flexibility to answer complex business questions efficiently and effectively. Many times business requirements overtake the basic aggregate measures of an OLAP cube and require customized measures that may or may not be non-additive. For example, Percent of Totals, Weighted Averages, and Ratios across time cannot be determined through the standard aggregation definitions of OLAP. The creation of custom OLAP member sets may be necessary to present these custom measures. These custom sets can present specific members within multiple levels of a hierarchy or across many dimensions. MDX allows for the creation of many highly customized measures, members, and sets but has its limits. There are ways to move beyond these limits and create more dynamic and complex functions using SAS Macros in conjunction with custom MDX | sas base, raw data, proc step, macro variable | source | 9 |
| 182 | Migrating SAS Enterprise Guide Projects from SAS 9.1 to SAS 9.2 | Shane Gibson | Migrating SAS Enterprise Guide projects from SAS Enterprise Guide 4.1 (which runs on SAS 9.1) to SAS Enterprise Guide 4.2 or SAS Enterprise Guide 4.3 (which both run on SAS 9.2) is a relatively straight forward process. But there are a few "tricks for young players" that if you are not aware of will extend the effort required to successfully migrate. If you are faced with migrating hundreds of user -created SAS Enterprise Guide projects, then you need to know these tricks. This presentation outlines the options for migrating SAS Enterprise Guide projects and recommendations on when to use each approach | sas enterprise guide, libname statement | source | 12 |
| 183 | Modeling Financial Risk Factor Correlation with the COPULA Procedure | Jan Chvosta, Donald Erdman, Mark Little |
Two keys to measuring and controlling the risk inherent in financial securities are (1) understanding the volatility of economic factors on which the value of the portfolio depends and (2) understanding how changes in those economic factors are related to each other. Recent progress in the mathematical technique of "copula" functions offers a powerful new approach to modeling dependencies among numerous risk factors. This paper explains how the new SAS/ETS COPULA procedure performs copula modeling and shows examples of using copula models for risk management problems |
probability distribution, correlation, proc univariate | source | 14 |
| 184 | Modeling Percentage Outcomes: The %Beta_Regression Macro | Christopher J. Swearingen, Maria S. Melguizo, Zoran Bursac |
Data are often encountered that fail the normality assumption, creating a dilemma between implementing the most appropriate analysis while maintaining appropriate inference. A recent extension of generalized linear models includes support for the Beta distribution, a flexible and accommodating distribution. SAS can implement "Beta Regression" through PROC NLMIXED, allowing the model's likelihood to be specified in terms of its mean and a variance component, providing a flexible modeling tool and providing intuitive inference. We provide a brief theoretical introduction to Beta Regression as well as a macro that implements Beta Regression and provides residuals plots for model fit diagnostics. KEY WORDS: Beta Regression, Beta Distribution, PROC NLMIXED, Macro, Generalized Linear Model |
regression analysis, estimate, beta distribution, response variable, likelihood function, proc step, predictor variable, clinical endpoint, clinical trial, maximum likelihood estimation, proc genmod | source | 12 |
| 185 | Multifrequency Forecasting with SAS High-Performance Forecasting Software | Michele A. Trovero, Ed Blair, Michael J. Leonard |
Forecasters often deal with data accumulated at different time intervals (for example, monthly data and daily data). A common practice is to generate the forecasts at the two time intervals independently so as to choose the best model for each series. That practice can result in forecasts that do not agree. This paper shows how the SAS High-Performance Forecasting HPFTEMPRECON procedure uses the lower- frequency forecast as a benchmark to adjust the higher-frequency forecast to take the best advantage of both forecasts |
proc freq, proc step | source | 10 |
| 186 | Multivariate Data Displays for Evaluating Clusters | Robert G. Moore | This paper illustrates some SAS graph techniques for displaying multidimensional data to facilitate visual evaluation of clusters. When performing a cluster analysis to identify customer segments, most clustering algorithms leave the number of clusters and the best clustering solution to the judgment of the analyst. Graphical techniques that display summary statistics for a number of variables simultaneously for all the clusters can be a helpful technique for visual evaluation of the clusters. The multivariate graphical displays illustrated in this paper include profile plots, Andrews plots, star charts, and Chernoff faces. The first three types are implemented using the PROC GPLOT and PROC GCHART procedures. Some preliminary data preparation is performed using the PROC MEANS and PROC TRANSPOSE procedures |
proc step, proc gchart, cluster analysis, proc means, binary variable, data visualization, proc gplot, sample, proc transpose | source | 9 |
| 187 | My Reporting Requires a Full StaffHelp! | Erin Lynch, Daniel O'Connor, Himesh Patel |
With cost cutting and reduced staff, everyone is feeling the pressure to finish their jobs. Preparing reports of any kind can be time-consuming. Not to worry; SAS is here to the rescue. This presentation goes over a real-world example of generating complex reports for schools. You see how SAS software can create polished and detail-rich academic charts and graphs that can be automatically customized by school. Using the power of ODS and SAS/GRAPH procedures, you can easily display data in a custom report that has never looked so good. Examples and code in this presentation help you generalize the usage. These same techniques can be applied to any industry |
proc step, proc fcmp, excel, proc sgplot | source | 12 |
| 188 | New Features in SAS/OR 9.3 | Edward P. Hughes, Manoj Chari |
SAS/OR 9.3 adds a variety of new and enhanced operations research capabilities. This paper surveys these added capabilities, highlighting their advantages and benefits. In optimization, new nonlinear and quadratic solvers provide improved performance, and a multistart algorithm for nonlinear solvers enables you to search more effectively for globally optimal solutions to difficult problems. SAS Simulation Studio adds support for 64-bit Windows environments and enhances graphical plots and icons, model editing and viewing, and distribution fitting with JMP software. The CLP procedure enhances its activity-scheduling capabilities and adds support for a linear objective function, providing an alternate means of optimization |
proc step, odds ratio, dataset, constraint programming, linear programming, poisson distribution, probability distribution, nonlinear programming, sample | source | 16 |
| 189 | New Light through Old Windows: Delivering Real-Time Information with Microsoft Windows 7 Desktop Gadgets | David Shannon | In this paper the author demonstrates how to deliver SAS information directly to the information consumer's desktop with Microsoft Windows 7 Desktop Gadgets. Displaying headline analytics from continually updating data, such as an active marketing campaign, or simply the status of your SAS server are just two possibilities for exploiting the Gadget feature. From Windows 7, Gadgets are an integral component in the operating system presentation. Real time (or near real time) information can be delivered by integrating a SAS session via web services or the SAS Integration Technologies product. This paper presents the steps for integrating SAS and Windows Gadgets. Worked examples, with source code, are demonstrated along with options for deployment. Finally, the pros and cons of delivering information this way are considered | web service, proc step | source | 12 |
| 190 | No Do-Overs: Tips for a Smooth SAS Enterprise BI Server Implementation | Tuesdi Helbig, Chris James, Gina Huff |
Like many technology-based projects, you get only one chance at implementing the SAS Enterprise BI Server at your organization. Coupled with a lack of knowledge about business intelligence, having to get it right the first time can make implementation a daunting task. In August 2010, the Office of Institutional Research at Western Kentucky University unveiled our portal using SAS Enterprise BI Server. While we had almost 50 years of combined SAS programming experience, understanding how to make SAS Enterprise BI Server work at our institution was somewhat overwhelming. Now that we are on the other side of a successful implementation, it is easy to see the steps we took that made the project successful and the missteps we would correct if we could do it over again. For those considering or implementing SAS 9.2 Enterprise BI Server, we would like to share our tips for making SAS Enterprise BI Server implementation a smooth process | business intelligence, sas consultant | source | 8 |
| 191 | Nobody Warned Me! Be Careful with SAS@: Invisible Errors | Ieva Brauksa | This paper can be useful for SAS programmers with different experience levels. It points out several logical and programming errors, sometimes called SAS gotchas, which can lead you to incorrect results without producing warnings or errors that would be easy to spot in the log. These mistakes arise from misinterpreting coding principles in a way that SAS OnlineDoc or the SAS System Help don't warn you to be careful about | proc step, proc contents, proc transpose | source | 7 |
| 192 | Now You Can Annotate Your Statistical Graphics Procedure Graphs | Dan Heath | Have you ever had a situation where you wanted to add custom labeling or other graphical features to your statistical graphics (SG) procedure output but could not find a way to do it? Even with all of the plot types and options of the SG procedures, you might have run into this situation. With SAS 9.3, you can add these features directly by using the new annotation support in the SG procedures. The annotation data set definition used by these procedures has been redesigned to take advantage of ODS Graphics functionality. This paper will discuss this functionality in detail, highlighting new features in this system that include the following: rich text support (including superscripts, subscripts, and Unicode) transparency support for all primitives more drawing spaces for easier placement of annotations |
bar chart, forest plot, proc sgplot, proc step | source | 18 |
| 193 | Numeric Length in SAS: A Case Study in Decision Making | Paul Gorrell | Storage space for data is always at a premium, so any effort to reduce the size of SAS data sets is easy to justify. One common option is to reduce the length of numeric variables. SAS documentation includes 'largest integer' tables to guide length specification, and macros have been created to automate the process. But reducing numeric length is rarely the most effective option. Understanding why requires a practical understanding of (i) how SAS stores numeric variables, (ii) how variable attributes are inherited by other data sets via merges or concatenation, and (iii) the difference between actual and permissible variable values. This paper reviews these aspects of SAS data sets and argues that the compress=binary option provides both superior file size reduction and improved i/o times for more efficient data processing. In contrast, reducing numeric length cannot match sas compression in reducing file size-a nd it has no effect on computational efficiency | numeric variable, length statement, decimal, cpu time, dataset, proc step | source | 12 |
| 194 | Obesity in Children Treated in New York City Public Hospitals | Ronald B. Low, Shunsuke Ito, Caroline Jacobs, Raymond Gregory, Susan Meehan, Ramanathan Raju |
Introduction: Childhood obesity is a large and growing health problem in the US and in many other parts of the world. Latest CDC estimates are of 17% prevalence of obesity among US adolescents. Childhood obesity is associated with health complications for the affected child, notably diabetes. Type II ("Adult Onset", or "Insulin Dependent") diabetes was rarely observed in children until the current epidemic of childhood obesity. Childhood obesity is a major risk factor of adult obesity, with its associated morbidity and mortality. New York City and State have made efforts to combat this epidemic. Some recent efforts include requiring restaurants to post calorie counts along with prices and a proposal for a special tax on sugary drinks. Estimates of the prevalence and cost of obesity are considerations in the New York debate. Our office was asked to estimate the prevalence of obesity among the children cared for, largely at public expense, by the public hospital system in New York City. Those hospitals are operated by a city owned corporation, the New York City Health and Hospitals Corporation (HHC). HHC does not routinely measure body fat to identify obesity; we do routinely measure height and weight in children. From this, one can calculate body mass index (BMI), an easily calculated and widely used objective measure of underweight, normal weight, overweight and obesity. Once BMI is calculated, it can be compared to age and sex specific charts or tables to classify the patient's weight status. These tables and charts define "obese" children as those with a BMI index on the 95th percentile of the reference population, based on "Z scores." BMIs are not normally distributed and the "Z score" has been modified from its standard definition of part of a normal distribution. Individuals with large muscle mass will have a high BMI and may be misclassified as obese, but this misclassification is relatively rare and the index can be used for population studies such as ours. | proc freq, proc step | source | 9 |
| 195 | ODS Output Data Sets That Work for You | Stuart D. Long, Edward Heaton, Daniel R. Scharf |
Although ODS Tables, combined with the ODS TRACE and ODS Output statements, provide a consistent method for retrieving statistics generated by SAS Procedures, the design of these tables is inconsistent. Many of the ODS Output data sets created with ODS T ables revolve around statistics describing one specific variable or groups of variables. We commonly use only a subset of the data found in a SAS Listing when reporting or continuing an analysis. Each of these data sets could be streamlined to contain th ese values plus additional descriptive information concerning these variables of interest -- such as the variable name, value, label, and formatted value. This paper proposes a consistent data structure for these data sets that better facilitates reportin g and analysis. It demonstrates a method to translate the data in the ODS Tables into this format |
proc step, proc logistic, logistic distribution, proc univariate, proc freq, out=, proc transpose, proc contents, in=, ods output, nominal variable, response variable, character variable, output, label statement, interval variable, numeric variable, confidence interval, hash table, dataset, proc means, macro definition, class variable, logistic regression, sysrc, data cleaning, macro variable | source | 29 |
| 196 | ODS RTF: The Basics and Beyond | Lauren E. Haworth | RTF is one of the best ODS destinations for sharing output with other users. All they need is a word processor to view your output. It also gives you and your users the ability to edit the output, add comments and other material, or combine the output with other reports. This paper focuses on key techniques to get you up and running with RTF output, and some extra tricks you can use to impress the boss. It covers the basics: headers, page orientation, margins, page breaks, and page numbers. It also shows how to upgrade your RTF output with images, explanatory text, hyperlinks, and a table of contents. Finally, this paper shows how to use RTF control words and fields to implement advanced features |
ods rtf, proc step, options statement, sas base, proc tabulate, title statement, sample, by group processing, ods html | source | 19 |
| 197 | On Deck: SAS/STAT 9.3 | Maura E. Stokes, Robert N. Rodriguez, Funda Güneş |
SAS/STAT 9.3, coming soon to a site near you, delivers numerous enhancements to the statistical software. The PHREG procedure supports frailty models for incorporating random effects in Cox regression, and the MCMC procedure provides a RANDOM statement to facilitate fitting Bayesian models with random effects. The NLIN procedure has been updated, and the MI procedure offers additional flexibility by providing a fully conditional specification method. The new SURVEYPHREG and HPMIXED procedures are also outfitted with additional capabilities. This talk reviews the highlights of SAS/STAT 9.22 and then describes important SAS/STAT 9.3 enhancements with practical illustrations, mainly from the SAS/STAT 9.3 documentation |
poisson distribution, likelihood function, proc mixed, sample, prior probability, maximum likelihood estimation, poisson regression, proc step, regression analysis, estimate, proc phreg, response variable, zero-inflated poisson, confidence interval, logistic distribution, proc freq, binomial distribution, numeric functions, proc nlin, proc genmod | source | 20 |
| 198 | Optimization of Gas-Injected Oil Wells | Robert N. Hatton, Ken M. Potter |
Artificial gas injection into aging wells boosts reservoir pressures, allowing for higher production rates. For constrained gas flows and multiple wells, the solution of this problem becomes difficult and time intense. With SAS/OR optimization techniques, a scalable (from 1 to n well) solution can be used to provide quick results. This paper provides background on artificial injection to orient the reader, theory on the mathematical formulation of the optimization, and the SAS code with results |
predictor variable, proc step, proc nlin, odds ratio, sample | source | 11 |
| 199 | Optimizing Clinical Research Operations with Business Analytics | David Handelsman | SAS is widely accepted as the gold standard in determining safety and efficacy for clinical trials, and provides the primary mechanism for preparing data for these traditional clinical research analysis activities. Most traditional SAS users in the pharmaceutical industry, however, are unaware of the broad range of SAS analytics that are widely applied in other industries. This paper will discuss and describe how SAS business and advanced analytics can be used to: design better trials target patient recruitment select the best investigator sites improve resource projections * optimize other operational activities These operational activities are historically managed through experience and spreadsheets. Experience and spreadsheets rarely provide the quantitative insight to make the best and most informed decisions | clinical trial, clinical research, data cleaning, life sciences, likelihood function | source | 6 |
| 200 | Order, order please: sorting data using PROC REPORT | Lisa Fine | PROC REPORT is one of the more useful SAS tools for generating formatted reports. One of the key expectations for customers of our reports is providing output in a sorted order. Accomplishing that sorted order is not always obvious or easy. This paper clarifies and demonstrates various sorting options and techniques for achieving a desired sort order when using the REPORT procedure. The paper presents the difference between ORDER and ORDER= options and details how the two options should be used together. The available types of sorts, how to sort multiple columns when display order differs from sort order, and techniques for creating a sort variable, one which incorporates the use of an alias are also discussed |
proc report, proc step, sample, proc format | source | 9 |
| 201 | Output Delivery System (ODS) - Simply the Basics | Kirk Paul Lafler | Are you looking for ways to improve or enhance the appearance of your SAS output? Output Delivery System (ODS) can help turn tired-looking output into great-looking information that has a purpose. Gone are the days when the only available formatting choice was the boring output listing that contained lifeless monospace fonts. ODS introduces exciting features that improve the appearance of your output. Us ing built-in format engines, ODS provides SAS users with a powerhouse of exciting capabilities to produce publishing-quality output. This presentation provides an introduction to ODS, including basic operation and syntax; how specialized output can be cre ated, including RTF, PDF, HTML, Microsoft EXCEL spreadsheets, and SAS data sets; and how selection (or exclusion) lists can be constructed to handle basic content customizations |
proc univariate, output delivery system, sas output, ods pdf, ods html, ods rtf, dataset, proc step | source | 14 |
| 202 | Parlay Standard ODS HTML Pop-Up Boxes into Pop-Up Jackpots | Sean Hunt | Effective dashboards balance the volume of displayed information with the inevitable need for additional (drill-down) data. One strategy to meet these goals is to employ pop-up boxes to convey drill-down data without having to navigate elsewhere. However, standard SAS Output Delivery System (ODS) HTML output produces pop-up boxes with limitations (i.e., text formatting and display time). By injecting some simple JavaScript into SAS ODS output, the author will demonstrate examples of turning standard pop-up boxes into blank canvases for instantly displaying data tables and graphs. The same concepts can also be used to add dynamic menu systems and other functionality to standard ODS HTML output |
ods html, dataset, sas output, sample | source | 6 |
| 203 | PATH and LISMOD Languages in SAS PROC TCALIS for Multigroup Comparisons | Fei Gu, Wei Wu |
This article introduced a new SAS procedure, PROC TCALIS, in structural equation modeling (SEM). In the previous version of SAS, PROC CALIS was incapable of handling multigroup models, whereas in SAS 9.2, PROC TCALIS, is enhanced with many new features such as capability for multi-group analysis and for accommodating different model specification languages used in other major SEM software packages (e.g., EQS, Mplus and LISREL). Two types of modeling languages are illustrated in a multigroup measurement model with and without equality constraints across groups. Fit indices and parameter estimates from SAS are compared to those from Mplus and LISREL. The fit indices from SAS are essentially consistent with those from Mplus but not those from LISREL. The parameter estimates are identical across the three software packages |
proc step, covariance, degrees of freedom, estimate, sample, proc calis, sample size, confidence interval, factor loading, latent variable, macro definition, maximum likelihood estimation, monte carlo | source | 10 |
| 204 | Patron Analytics in the Casino and Gaming Industry: How the House Always Wins | Scott W. Sutton | To stay ahead in the ultra-competitive Las Vegas Strip, casinos have to engage in predictive analytics to segment and market to their patron databases. Predicting the future worth of gamblers is the ultimate goal of analytics in the casino industry. The gaming industry is inherently suited to advanced analytics due to the vast amount of data and information collected within the casino industry. This paper will discuss how to leverage your patron database to effectively segment and market to your customers. Specifically, it will discuss how an analysis of patron spending in gaming, hotel, and retail can be used to predict the future value of your patrons. Furthermore, once you've identified those patrons, we'll discuss how to optimize your marketing to those patrons | likelihood function, data mining, regression analysis, proc hpsplit, logistic regression, logistic distribution, proc step, proc freq, sas enterprise guide | source | 7 |
| 205 | Performance Benchmark with SAS 8.2, SAS 9.1.3, and SAS 9.2 on UNIX SPARC Architectures | Edmond Cheng | In the rapidly growing pace of information technology advancement, making the right decisions about software upgrades and hardware purchases becomes an important factor in maximizing one's SAS investment. With a wide range of SAS products and infrastructure platforms available, one challenge faced by SAS administrators is choosing the optimal bundle in software selection and hardware configuration. The case study presented in this paper evaluates the performance level of Base SAS product versions, SAS 8.2, SAS 9.1.3, and SAS 9.2, on selected UNIX SPARC server architectures. In a controlled environment, a series of performance simulations were executed to quantify input/output time, CPU time, and other process statistics among various SAS versions and UNIX configurations. The benchmark measurement provides first-hand tangible insights for administrators and managers in making informed SAS purchases and upgrade decisions to meet specific needs | cpu time, dataset | source | 7 |
| 206 | Perl Regular Expression in SAS Macro Programming | Yanwei Zhang | In this paper, the Perl regular expression facility that provides a concise and flexible means for matching strings of text is extended to the macro environment using three new macro functions. Consequently, this allows direct pattern matching and replacement in macro variables, facilitating the construction of flexible and customized functions through internally parsing patterned inputs. This is illustrated with a specific example where we outline the key steps to build an automated variable selection process in the generalized linear models, with additional capability to perform joint test of several variables such as spline bases | poisson distribution, feature selection, degrees of freedom, macro variable, macro function, macro put, proc genmod, macro definition, regression analysis | source | 7 |
| 207 | Plate of Spaghetti Anyone? Techniques for Learning Existing SAS Programs | Patricia S. Aanderud | When starting a new job or project, a programmer may receive existing programs that need modifications. Often the code's original intent has mor phed over the years and many other progra mmers have made minor changes to it. There may be little documentation to explain the code, the job setup, or even how to run it | proc step, proc freq, if-then-else, proc sql | source | 10 |
| 208 | Plotting an Error and Line Plot and a Bar Graph in a Single Plot with Dual Y-Axis Scales | Sanjiv Ramalingam | In analyzing clinical trial data, it is sometimes ideal to vi sualize the changes in the effica cy parameter with respect to change in dosing over time. This facilitates to observe the relationship between the two parameters and to also provide dosing recommendations, i.e. dose selection and c hoice of dose interval. A bar graph representing the dose and an error bar to represent the change in the efficacy pa rameter would clearly illustrate the relationship between the two parameters over time. The paper combines PROC GPLOT and the ANNOTATE feature to implement the above graph. A very simple and robust method is described to draw the bars and the pattern within every bar in a few steps |
bar chart, proc gplot, proc step | source | 6 |
| 209 | Point-and-Click Programming Using SAS Enterprise Guide | Mira Shapiro, Kirk Paul Lafler |
SAS Enterprise Guide empowers organizations exploiting the power of SAS by offering programmers, business analysts, statisticians and end -users with powerful built-in wizards to perform a multitude of reporting and analytical tasks, access multi-platform enterprise data sources, deliver data and results to a variety of mediums and outlets, perform important data manipulations without the need to learn complex coding constructs, and support data management and documen tation requirements quickly and easily. Attendees learn how to use the graphical user interface (GUI) to access tab -delimited and EXCEL input files; subset, group, and summarize data; join two or more tables together; flexibly export results to HTML, PDF and EXCEL; and visually manage projects using flowcharts and diagrams |
excel, sas enterprise guide, dataset, proc means, data governance, delimited file, sas output, proc sql, proc step | source | 18 |
| 210 | Predictive Analysis of Auto Insurance Purchasing Behavior on the Internet | Roosevelt Mosley | Today, there is much information available about current and potential auto insurance customers. This information can be used to perform more detailed analyses to predict the purchasing behavior of certain types of customers and to determine what drives that behavior. This paper will discuss the analysis of two stages in the customer purchase decision. 1. Submitting a Quote - for those c onsumers that visit an insurance company website, what is the likelihood that they complete the quote process and submit the quote to the company 2. Purchasing a Policy - of those consum ers that submit a quote, what is the likelihood that they ultimately purchase a policy from the company The results of these types of analyses can be instrumental in helping to set marketing, pricing and customer service strategy. Potential insureds with a higher likelihood of purchasing can be identified, and marketing strategies can be put into place that optimize the marketing dollars spent | likelihood function, insurer, auto insurance, proc freq, proc hpsplit, response variable, regression analysis | source | 15 |
| 211 | Present Graph Text-Based Components Using SAS 9.2 Graph Template Language (GTL) | Zhuoye Xu | SAS/GRAPH 9.2 includes ODS Graphics and traditional SAS/GRAPH. Both SAS statistical analysis procedures and SG procedures have Graph Template Language (GTL) executed as background for graph generation. GTL is a powerful and flexible tool to create the customized graphs with different customized text-based components, especially when SG procedures and statistical analysis procedures cannot meet your needs. In the traditional SAS/GRAPH, data driven text-based components are usually generated by annotation facility. In this case, any changes regarding the restructuring are relatively static and require quite complicated annotation coding change rather than dynamic updates. SAS/GRAPH 9.2 ODS Graphics does not support annotation facility however certain powerful features available in ODS Graphics GTL is equally or even more effective than the traditional SAS/GRAPH annotation facility |
sample, scatter plot, proc means, outlier, graph template language, in=, regression analysis | source | 19 |
| 212 | PROC COPY and PROC APPEND: A Developer's Secrets | Diane Olson | This presentation gives you the inside tricks about PROC COPY and PROC APPEND that the developer is dying to tell you. Want a clone of your data set (attributes and data) written to the same library? You can! Want to copy your data set using data set options? You can! Need a data set converted to a particular data representation and encoding? You can! Make the most of these basic, necessary procedures by applying the tricks you'll learn |
proc step, proc append, proc copy, constraint, options statement, data integrity, elapsed time, audit trail, dataset, proc contents | source | 6 |
| 213 | PROC DATASETS: The Swiss Army Knife of SAS Procedures | Michael A. Raithel | The DATASETS procedure provides the most diverse selection of capabilities and features of any of the SAS procedures. It is the prime tool that programmers can use to manage SAS data sets, indexes, catalogs, etc. Many SAS programmers are only familiar with a few of PROC DATASETS's many capabilities. Most often, they only use the data set updating, deleting, and renaming capabilities. However, there are many more features and uses that should be in a SAS programmer's toolkit. This paper highlights many of the major capabilities of PROC DATASETS. It discusses how it can be used as a tool to update variable information in a SAS data set; provide information on data set and catalog contents; delete data sets, catalogs, and indexes; repair damaged SAS data sets; rename files; create and manage audit trails; add, delete, and modify passwords; add and delete integrity constraints; and more |
dataset, proc step, constraint, audit trail, run, key, libname statement, foreign key, sas library, sas programmer, modify statement, proc datasets, sas log, proc contents, proc sql, attrib statement, data integrity, proc append | source | 25 |
| 214 | PROC PANEL Vs. A FAST ALGORITHM FOR ESTIMATION OF UNBALANCED PANEL DATA MODELS WITH TWO FIXED EFFECTS | Alan Ricardo da Silva, Mariana Fernandes Teixeira, Thaís Helena Fernandes Teixeira |
The panel data models are becoming more common in r elation to cross-section and time-series models due to innumerable advantages, in addition to the computat ional advance that facilitates their utilization. T his work aimed at presenting a faster general algorithm for estimatio n of panel data models with two fixed effects, when it is compared with PROC PANEL, working with balanced or unbalance d data. The results by simulation showed that until with a large data base (50,000 observations) only a few se conds were necessary for estimation, while in the PROC PANEL, the estimation wasn't possible due to out of memory |
proc panel, cpu time, proc step, fixed effects, binary variable, predictor variable, panel data, data standard, response variable, proc freq | source | 12 |
| 215 | PROC PRINT and ODS: Teaching an Old PROC New Tricks | Darylene Hecht | PROC PRINT is one of the oldest living procedures in SAS and is probably the most widely used procedure of all time. In SAS 9.3, the internals of PROC PRINT were completely rewritten to update it and make it fully compatible with the Output Delivery System (ODS). This paper presents many of PROC PRINT's new tricks to help you create more complex reports for multiple destinations, including HTML, PDF, RTF, and (of course) LISTING. Topics include styles, traffic lighting, PROC DOCUMENT to store and replay the output, the new SUMLABEL and BLANKLINE options, #BYVAL and #BYVAR substitutions to customize titles, and the BYID layout. You CAN teach an old PROC new tricks! |
proc step, proc print, by group processing, title statement, file statement, output delivery system, sas output, label statement | source | 22 |
| 216 | PROC TABULATE: A Getting Started Tutorial | Arthur L. Carpenter | Although PROC TABULATE has been a part of Base SAS for a very long time, this powerful analytical and reporting procedure is very under utilized. TABULATE is different; it's step statement structure is unlike any other procedure. Because the programmer who wishes to learn the procedure must essentially learn a new programming language, one with radically different statement structure than elsewhere within SAS, many do not make the effort. The basic statements will be introduced, and more importantly the introduction will provide a strategy for learning the statement structure. The statement structure relies on building blocks that can be identified and learned individually and in concert with others. Learn how these building blocks form the structure of the statement, how they fit together, and how they are used to design and create the final report will be presented |
proc step, proc tabulate, missing value, proc format, format statement | source | 18 |
| 217 | PROC TABULATE: Doing More | Arthur L. Carpenter | Although PROC TABULATE has been a part of Base SAS for a very long time, this powerful analytical and reporting procedure is very under utilized. TABULATE is different; it's step statement structure is unlike any other procedure. Because the programmer who wishes to learn the procedure must essentially learn a new programming language, one with radically different statement structure than elsewhere within SAS, many do not make the effort. The basic statements will be introduced, and more importantly the introduction will provide a strategy for learning the statement structure. The statement structure relies on building blocks that can be identified and learned individually and in concert with others. Learn how these building blocks form the structure of the statement, how they fit together, and how they are used to design and create the final report will be presented |
proc step, proc tabulate, missing value, proc format, format statement | source | 18 |
| 218 | PROC TABULATE: Getting Started | Arthur L. Carpenter | Although PROC TABULATE has been a part of Base SAS for a very long time, this powerful analytical and reporting procedure is very under utilized. TABULATE is different; it's step statement structure is unlike any other procedure. Because the programmer who wishes to learn the procedure must essentially learn a new programming language, one with radically different statement structure than elsewhere within SAS, many do not make the effort. The basic statements will be introduced, and more importantly the introduction will provide a strategy for learning the statement structure. The statement structure relies on building blocks that can be identified and learned individually and in concert with others. Learn how these building blocks form the structure of the statement, how they fit together, and how they are used to design and create the final report will be presented |
proc step, proc tabulate, missing value, proc format, format statement | source | 18 |
| 219 | Quantitative requirements for non-life insurance under Solvency 2 | Sverre Grevskott | Solvency 2 is an EU directive set out to strengthen capital adequacy and risk management for insurers. The directive gives the insurers an opportunity to develop internal model in order to quantify the capital requirements. In this work we give examples on how this can be done for a non-life insurance company in the major risk areas: operational, counterparty, catastrophe, market and insurance risk. Insurance risk is usually the largest risk in non-life and we demonstrate how SAS may be used to model this. Insurance risk is composed of underwriting risk and reserve risk, where underwriting risk is the risk arising from claims incurring in future accounting periods, and reserve risk is the risk arising from previous accounting periods. We demonstrate how these risks can be modeled and show an example on how copulas can be used to find non linear dependencies between lines of business | life insurance, solvency ii, insurer, market risk, reserve risk, underwriting risk, proc step, capital adequacy, proc genmod, correlation, proc model, gamma distribution, poisson distribution, pd, insurance claims | source | 8 |
| 220 | Race/Ethnic Differences in Carotid Stiffness and Diameter Using PROC GLM | Matthew S. Markert | Race/ethnic differences in the dilatation and stiffening of the carotid artery are unknown. These differences might be modified by age, an important vascular risk factor. Carotid diameters were assessed by ultrasound in 1116 subjects from the Northern Manhattan Study (NOMAS). Multivariate linear regression models were performed using PROC GLM (SAS 9.2) to determine the relationship between race/ethnicity and carotid arterial stiffness (STIFF), and between race/ethnicity and carotid diastolic diameter (DDIAM). PROC UNIVARIATE was used to test for normality assumptions. Interactions between race/ethnicity and age for STIFF and for DDIAM were also assessed. After multivariate adjustment for age, gender, body-mass index, hypertension, diabetes, LDL, HDL, and presence of carotid plaque, Hispanics had a lower DDIAM (p<0.05) compared to whites. However, a significant interaction between age and race/ethnicity was observed for both outcomes |
proc step, anova, proc univariate, normal distribution, proc anova, proc glm, proc genmod, regression analysis, response variable | source | 10 |
| 221 | Randomizing Small Strata Groups: Balancing Baseline Population Measures While Staying True to a Chosen Allocation Strategy, an Alternative to PROC SURVEYSELECT | Gregg M. Oginz | Essential components of a Stratified Randomized Control Trial are that, with respect to pre-period outcome measures, strata means are significantly different, study arm means are not statistically different and study arm populations stay true to the selected allocation strategy. When employed correctly, a sound stratification methodology will establish well balanced baseline populations. In simple terms, PROC SURVEYSELECT's method of randomizing stratum is to count out in repetition, arm assignments in accordance with the selected allocation. To illustrate, for a 4:1 allocation, 1,1,1,1,0 would be counted out and repeated over a "shuffled' stratum. When a stratum consists of five members, four are assigned to "Study Arm 1" and one to "Study Arm 2". But what happens when a stratum consists of nine members? PROC SURVEYSELECT counts out 1,1,1,1,0,1,1,1,1 over the nine "shuffled' members. The outcome is eight members assigned to "Study Arm 1" and one to "Study Arm 2" |
treatment arm, randomization, proc surveyselect, proc step, primary endpoint, random sampling, macro loop, proc sql, macro definition | source | 12 |
| 222 | Rasch Model and Beyond with PROC GLIMMIX: A Better Choice | Teck Kiang Tan, Wenshu Luo, David Hogan |
Rasch model is commonly used in education measurement and testing for evaluating tests and assessments. While there are various SAS procedures that could carry out item analysis, PROC GLIMMIX provides a relatively simple syntax for Rasch modeling and i t takes almost 500 times faster to estimate a regular Rasch model compared to PROC NLMIXED. This paper demonstrates how to estimate standard Rasch model using PROC GLIMMIX, and further illustrates the possibilities of using this procedure, under the multilevel fra mework, to estimate various Rasch models. The paper ends with Rasch models that incorporate covariates at person and item levels to explain the probability of getting a correct answer for an assessment |
proc step, proc glimmix, binary variable, logistic regression, logistic distribution, sample, proc mixed, response variable | source | 12 |
| 223 | Ratemaking Using SAS Enterprise Miner: An Application Study | Billie Anderson | Insurance companies gain competitive advantage by offering better rates and services to attract and retain the best customers. Generalized linear models (GLMs) have become popular and proven techniques for ratemaking and actuarial work over the past decade. Claim frequency is typically modeled using a Poisson distribution; severity is modeled using a gamma distribution; and pure premium is modeled using a Tweedie distribution. Insurance providers use these models to accurately estimate losses and set the most competitive rates accordingly. This paper ties the theory of ratemaking using GLMs to case studies that use real insurance data and shows you how to use SAS Enterprise Miner to model claim frequency, severity, and pure premiums | pure premium, poisson distribution, likelihood function, proc freq, data mining, sample, poisson regression, pure premium model, claim cost model, proc hpsplit, insurance claims, zero-inflated poisson, proc genmod, gamma distribution, interval variable, response variable, negative binomial distribution, binomial distribution, data cleaning, score code | source | 20 |
| 224 | Ready to Become Really Productive Using PROC SQL? | Sunil Kumar Gupta | Using PROC SQL, can you identify? - at least four ways to select and create variables? - at least five ways to create macro variables? - at least five ways to create or modify table structure? - at least five ways to change table content? Learn better and apply multiple PROC SQL programming options through task-based examples. This HOW will review topics in table access, retrieval, structure and content, as well as creating macro variables. References will be provided for key PROC SQL books, relevant webinars, podcasts as well as key SAS technical papers |
proc sql, macro variable, proc step, order by | source | 18 |
| 225 | Ready, Set, Retain, and then Maybe Reset | Lisa Fine | The RETAIN statement is one method that SAS programmers commonly use for making comparisons across observations. One source of misunderstanding around the RETAIN statement centers around how long a value is retained and the ability or need to reset retained variables in many circumstances. This paper clarifies, through examples, how the RETAIN statement overrides the default behavior of a DATA STEP and maintains a variable's values until the value is reset. The scope of the paper is to demonstrate the use of the RETAIN statement in conjunction with assignment statements |
retain, dataset, variable, proc print, proc step | source | 11 |
| 226 | Recreating SAS Data Sets via SAS Code Generation | Richard D. Langston | This paper describes a recent project where SAS code was needed that replicated exactly the contents of a SAS data set. The SAS code generator for this project takes into account various important issues such as n-literals and encoding problems. Several specialized functions such as CATS, KCVT, and VNAME are used and explained in the generator, along with special techniques for using _NUMERIC_ and _CHARACTER_ |
dataset, numeric variable, proc step, missing value, label statement, macro definition | source | 7 |
| 227 | Regression Model Development for Credit Card Exposure at Default (EAD) Using SAS/STAT and SAS Enterprise Miner 5.3 | Iain L. Brown | Over the last few decades, credit risk research has largely been focused on the estimation and validation of probability of default (PD) models in credit scoring. Only more recently, academic work has been conducted into the estimation of LGD (e.g. Bellotti and Crook, 2009, Loterman et al, 2009, Matuszyk et al, 2010). However, to date very little model development and validation has been reported on the estimation of EAD, particularly for retail lending (i.e. credit CARDS). Nonetheless, EAD and LGD are both important inputs to the Basel II capital calculations as they enter the capital requirement formulas in a linear way (unlike PD, which comparatively has a smaller effect on minimal capital requirements than LGD and EAD). Therefore, changes to EAD (and LGD) will have a crucial impact on the capital of a financial institution and as such also its long-term strategy. Hence it is important to develop robust models that estimate EAD as accurately as possible. |
logistic regression, regression analysis, correlation, credit card, ead, credit conversion factor, proc reg, lgd, credit scoring, data cleaning, response variable | source | 11 |
| 228 | Regression Testing: The SAS Programmers Friend | Philip James Busby | Consultants are often assigned to fix systems that were developed ad hoc over many years. The code needs consolidation, error checking, improvements for ease of use, and lots of comments. Above all, the consultant must ensure that the resulting system produces correct results. This paper shows how a reporting system SAS macro was made easy to use and easy to maintain by developing a regression test to ensure software quality. A regression test for a SAS/AF software system is described, using a single global SAS macro variable to make the testing of an interactive system run without any user intervention |
regression analysis, macro definition, macro variable, proc compare, proc step | source | 11 |
| 229 | Repeated Measures Analysis of Correlated Data with Multiple Responses Using SAS | Anbuchelvi Jeyabslasingham, Anpalaki J. Ragavan |
When several measurements are taken on the same experimental unit, the measurements tend to be correlated with each other. When the measurements are responses to levels of an experimental factor of interest, such as time or treatment, the correlation can be taken into account by performing a repeated measures analysis of variance. In addition, if there are multiple responses, the analysis requires methods of multivariate analysis combined with tools of repeated measures, which is a challenging task and requires careful attention to several details such as distributional variation among responses, correlations among responses, subject specific trend in the treatment variable. In this research, repeated measures analysis of correlated data with multiple response variables that are a mixture of continuous, count, and binomial is explored. The common problems that arise when analyzing such data are addressed in detail in SAS | response variable, proc step, repeated measures, proc mixed, repeated measures anova, proc glimmix, covariance, correlation, fixed effects, proc genmod, proc glm, anova, covariance structure, likelihood function, crossover study, multivariate analysis, estimate, binomial distribution, main effect, unstructured covariance, poisson distribution | source | 10 |
| 230 | Repo Your Missing Data Using PROC REPORT | Ethan Miller | PROC REPORT is a great procedure for producing reports suitable for clients. This paper covers the use of PROC REPORT to generate simple frequency counts and percents that include missing values (similar to PROC FREQ with the MISSING OPTION), and a second percent that excludes missing observation from the calculation (similar to PROC FREQ with the MISSPRINT OPTION). This paper will also include descriptions of how to (1) create totals using compute blocks; (2) format the report; (3) add titles, footnotes, subscripts, and superscripts; and (4) specify various output options |
proc report, proc step, proc freq, proc tabulate, missing value | source | 5 |
| 231 | Romancing Your Data: The Getting-to-Know-You Phase | Carole Jesse | The ability to gather and store large amounts of data has become commonplace. As these capabilities have grown, so has database complexity. The deluge of corporate acquisitions further complicates things, driving more undocumented data sources into the hands of new database users, often without the institutional knowledge to go with them. Thus, analysts must develop the ability to discover the complexity of a database on their own. This paper focuses on Oracle databases and use of SAS/ACCESS Interface to ORACLE to explore the Oracle Data Dictionary Views that contain the database metadata. We focus on metadata summaries of interest to a user new to the database. The metadata is presented through a series of programs that use Base SAS, specifically the SQL Procedure, macro language, and the Output Delivery System (ODS). The target audience for this paper includes anyone who uses Oracle databases to solve business problems |
oracle, macro variable, proc sql, data dictionary, oracle access, ods html, proc step, key, sample, libname statement, sql pass-through, sas enterprise guide | source | 12 |
| 232 | Run Your SAS Job Faster: Parallel Processing with Desktops on LAN | Sijian Zhang, Robert Brown, Yu Zhang |
We all want to run SAS jobs faster, especially the large ones. The common solution is to use more powerful machines, which are not cheap. However, in some circumstances, the similar effect can be achieved without adding any hardware. If a SAS job is dividable, and each section can be run independently; and on the local area network (LAN), other computers with SAS installed can be accessed with right user permission, then this parallel method can be applied to speed up the processing. This paper discusses some features of command psexec.exe, SAS job division, and load balancing. Through the experimental tests and a real case application, the key programming skills are explained, and the significant speed increase effect is illustrated | parallel processing, macro loop | source | 9 |
| 233 | SAS & Circos: Creating Circular Visualizations of Tabular Data | Danni Bayn Jr. | Representing complex data and relationships in a meaningful and enticing way is a timeless, but increasingly important, challenge for analysts. Circos is a highly customizable Perl module that can be used to create circular representations of tabular data that allow for improved visualization and enhanced insight. Unfortunately, it requires a fair bit of patience and a solid grasp of Unix and Perl to install and run Circos from the provided tutorials. That is where SAS comes in. Rather than go through the frustration of installing dependencies and manually modifying a slew of textual configuration files, the program presented here does all of it for you. With this program, SAS users can take existing data files and quickly transform them into stunning circular visualizations. The intended audience for this paper is SAS developers with a working knowledge of SAS and a basic understanding of Perl. Code was developed with SAS 9.1.3 running under Windows XP Professional and Circos version 0.52-2 under Perl v5.10. Final code was tested under SAS 9.2. | source | 10 | |
| 234 | SAS Analytic Solutions Win in Response to the Credit CARD Act of 2009 | Rex Pruitt | This paper is about how SAS software is helping PREMIER Bankcard, LLC overcome the regulatory impact of the recent "Credit CARD ACCOUNTABILITY RESPONSIBILITY AND DISCLOSURE (CARD) Act of 2009" legislation. In brief, the "CARD Act of 2009" most significantly impacts the sub-prime credit card industry as foll ows: "Prohibits Exorbitant and Unnecessary Fees" "New rules regarding rates, fees, and limits" In response to these specific legislated items, PRE MIER's leadership has heightened its reliance on bu siness analytics and intelligence because they are driven to win. SAS is a tool of choice in providing the r apid response necessary in the delivery of extremely important an alytics that the executive team consumes and uses t o steer PREMIER past this macro-economic circumstance. In this paper I will describe in further detail the SAS solutions that PREMIER is using to win the CARD Act war | credit card, sas base, data mining, output delivery system, proc format, proc tabulate, macro definition, proc ucm | source | 9 |
| 235 | SAS Business Intelligence: Using Enterprise-Level Solutions to Meet Department-Level Goals | Keith Myers | Even though enterprise business intelligence focuses on the enterprise, you are not required to use these tools at just the enterprise level. Information exists at all levels of an organization, and some of the most useful information often can be found at the departmental level. This paper presents a case study of a reporting initiative that was implemented within a SAS Technical Support department. The department's goal was to enable each employee to create in Microsoft Word a monthly status report that included individualized monthly metrics. The metrics were derived, incorporated, and presented by using several SAS Business Intelligence solutions: SAS Information Map Studio, SAS Web Report Studio, SAS Stored Processes, and the SAS Add-In for Microsoft Office. The custom report also featured a new solution for using a stored process to pass prompt values to multiple sections of a report in SAS Web Report Studio | business intelligence, sas management console, bar chart, dataset | source | 15 |
| 236 | SAS Code and Macros: How They Interact | Bruce F. Gilsen | SAS users at every level of experience seem to struggle to understand the interaction between SAS code (DATA and PROC steps) and macros. Understanding this interaction enables SAS users to understand existing applications and to take better advantage of the tools offered by SAS software and develop code much more effectively. This important concept is first reviewed in a somewhat informal way, similar to the one-on-one discussions I have had many times with Federal Reserve Board SAS users, and then with a series of small examples. |
macro variable, macro loop, do loop, macro facility, &variable, macro definition, proc step, sas log, rename statement, macro flow control, call symput, put statement, %if-%then-%else, proc sql, call execute, automatic macro variable, proc datasets, data step, dictionary tables, macro put | source | 20 |
| 237 | SAS Data and Stored Processes on a BlackBerry | Sy J. Truong | In the competitive smart phone environment, RIM BlackBerry remains the leading smart phone worldwide changing how we use mobile computing. Although both the iPhone and Android have been making the headlines, BlackBerry is quietly growing in the area of mobile applications for business use with its | macro definition, dataset, business intelligence, sample, macro parameter, sas analyst, proc step, sas library | source | 11 |
| 238 | SAS Data Management: How Raw Variables with Complex Skip Patterns and Missing Values Are Interpreted with Derived Variables | Isabel H. Perry, Samantha E. Dilworth, Elise D. Riley |
Shelter, Health, and Drug Outcome in Women (SHADOW) is a research study with the Department of Medicine at the University of California, San Francisco that has been gathering quantitative information through biannual interviews w ith impoverished women in San Francisco. The project seeks to understand how competing needs influence the health and HIV risk behaviors of homeless women in order to inform healthcare delivery and social services. The focus of this paper is to employ data management techniques in SAS to create common variables of interest that may be requested by study investigators. A question of particular importance is whether a participant partakes in serodiscordant sex (i.e. sex with a man of the opposite HIV status). Roughly half of the 301 women in the study are HIV positive, and multiple conditions must be met for a person of either serostatus to be eligible for the outcome | proc step, array, proc freq, if-then-else | source | 10 |
| 239 | SAS DICTIONARY: Step by Step | Patrick Thornton | This paper offers a step-by-step visual review of the SAS 9.2 DICTIONARY tables for the beginning to intermediate SAS user who is generally unfamiliar with PROC SQL and macro programming. The content of the DICTIONARY tables (such as data set names; variable names, labels, and types; option names and values; titles and footnotes; format and macro program catalog entries; and external file and library paths) is visually explored using common procedures and SASHELP views. This paper demonstrates that the DICTIONARY is easily accessible and shows many valuable uses of the DICTIONARY. This paper also shows some alternative techniques for obtaining metadata when applicable and directs users to papers that feature advanced techniques using the DICTIONARY |
proc step, proc print, proc contents, sashelp, macro variable, proc tabulate, excel, dictionary tables, libname statement, work library, proc report, where statement, options statement, macro definition, proc sort, proc sql, call execute, index | source | 22 |
| 240 | SAS Enterprise Guide 4.3: Finally a Programmer's Tool | Marje Fecht, Rupinder Dhillon |
Have you been programming in SAS for a while and just aren't sure h ow Enterprise Guide can help you? It isn't just a pretty face! This presentation demonstrates how SAS programmers can use SAS Enterprise Guide 4.3 as their primary interface to SAS while maintaining the flexibility of writing their own customized code . We explore: navigating the views and menus using Enterprise Guide to access your existing programs and enhance processing exploiting the enhanced development environment including syntax completion and built -in function help utilizing Code Analyzer, Report Builder, and Document Builder adding Project Parameters and dynamic parameters to generalize the usability of programs and processes leveraging built-in capabilities available in SAS Enterprise Guide to further enhance the information you deliver. Audience: SAS users who understand the basics of SAS programming and want to learn how to use Enterprise Guide | sas enterprise guide, sas programmer, bar chart, excel, proc step, work library, dataset, sas log, macro variable, temporary dataset | source | 24 |
| 241 | SAS Enterprise Guide for All! | Steven J. First, Jennifer F. First |
SAS Enterprise Guide is for everyone! Analysts can immediately begin working with SAS Enterprise Guide to perform analysis and generate reports, all without knowledge of the SAS language. Experienced programmers can leverage SAS Enterprise Guide's functionality for improved efficiency and methodology. Users can also use SAS Enterprise Guide to simplify organizing SAS processes, graphing, reporting, and developing flexible systems. There are a few potential hurdles and considerations for migrating to SAS Enterprise Guide, but they can be easily overcome with planning and a clear ROI. SAS Enterprise Guide will allow your organization to increase efficiency, leverage unutilized functionality, and streamline your processes! | sas enterprise guide, excel | source | 8 |
| 242 | SAS Enterprise Guide Options: Let the Tools Work for You | Harsha M. Kotian, Susan M. Jumper |
The purpose of this paper is to communicate to SAS users the broad range of Options contained within SAS Enterprise Guide. Located under the Tools menu, the Options item on the dropdown list provides a diverse assortment of settings available for specialized application development. Choices offered to the user include those impacting the SAS environment, servers, tasks, results and data. Also explained are the selections available when applying individualized user preferences. Topics covered herein are applicable across all supported operating systems. This presentation is designed not only for users new to Enterprise Guide and wanting to begin by using the most efficient methods of project creation and processing, but also for the intermediate user looking to increase project efficiencies in real world scenarios. The majority of these options have been utilized by the authors in overcoming particular work challenges they have encountered during project development and reporting | sas enterprise guide, out= | source | 20 |
| 243 | SAS Flirts with Java: PowerPoint Creation | Laiju Zhang | This paper presents the process for creating power point slides shows using SAS and Java. It describes how to create object from SAS DATA STEP by using the SAS recent addition of javaObject to its version 9, how to use this object created within SAS session to communicate with Java package apache poi hslf, thus creating power point slides shows. Further realizing the difficulties of communicating with external independent Java objects when creating and using Java objects within SAS DATA STEP, the paper explores the possibility of enhancing the power point creation process by letting SAS write Java programs that create the power points slides show |
sas programmer, txt | source | 9 |
| 244 | SAS Global Forum Conference iPhone App | Sy J. Truong | As information transitions from hidden databases and obscure networks into mainstream social networks like Twitter, Facebook and Youtube, users leverage the social aspects of communication to make sense out of the deluge of data. Social media can provide extra context and more meaningful information when it is recommended from a trusted friend as compared to spam. Attending SUGI in the past used to involve carrying a thick proceedings and a program book while deciphering what presentation is most relevant and useful for each attendee. As SAS conferences grow and the number of sections and papers become more diverse, this process becomes overwhelming. Since SAS Global Forum by its own nature is a social event, it lends itself to social networks as it helps individuals make sense and bring context to the presentations that they attend | dataset, macro variable | source | 7 |
| 245 | SAS Global Users Group Data Warehouse: A Look Behind the Scenes | Gregory S. Barnes Nelson | Wouldn't it be nice to see how someone built an entire end -to-end system (from data acquisition to data cleansing to a fully integrated data warehouse) using SAS ? This presentation shares some of the experience of designing and implementing a data warehouse. Topics include planning, architecture, data model, technology choices, and methods used in the creation of the platform . Special attention is paid to the challenge of data cleansing and enrichment and how we were able to engage the SAS community in helping us analyze this data . Data IntegrationSAS Global Forum 2011 2 | source | 22 | |
| 246 | SAS High-Performance Markdown Optimization | Henry Bequet | This paper presents an overview of the architecture of the SAS High-Performance Markdown Optimization solution. We begin with a brief description of the many-core chip architecture and explain why it is relevant to software development. We then describe SAS High-Performance Markdown Optimization and present the main design pattern of its parallel architecture. We also discuss a couple of deployment scenarios and show that this architecture has the flexibility of handling a broad spectrum of situations. We finally review the performance that we have measured in those deployment scenarios | high-performance sas, sample | source | 9 |
| 247 | SAS Macro Programming for Beginners | Susan J. Slaughter, Lora D. Delwiche |
Macro programming is generally considered an advanced topic. But, while macros certainly can be challenging, it is also true that the basic concepts are not difficult to learn. This paper is designed for people who know the basics of SAS programming, but know nothing about SAS macro programming. We explain how the macro processor works, and how to use macros and macro variables. Using these techniques you can create flexible, reusable code that can save you time and effort | macro variable, call symput, proc step, proc print, macro definition, cpu time, dataset, proc sort, sas log, if-then-else, options statement, %if-%then-%else | source | 12 |
| 248 | SAS OLAP Cubes Go Green: Reusable Shared Dimensions in SAS 9.3 | Jana Van Wyk, Ann Weinberger |
Do you find yourself needing to recreate the same dimension in multiple SAS OLAP cubes? SAS OLAP cube administrators and designers will be glad to know that, in SAS 9.3, you can create a dimension to be shared among multiple OLAP cubes. Re-using a shared dimension in multiple OLAP cubes helps ensure consistency and is also good for the environment, since it reduces the amount of metadata and member data stored. This paper includes an introduction to this new feature and shows how to integrate it into your current OLAP environment. A case study is presented that starts with an existing cube and modifies it to use shared dimensions. The case study includes the application of authorization and also the promotion of shared dimensions and cubes. Best practices for maintenance of shared dimensions and cubes are also discussed | proc step, dimension table, fact table | source | 18 |
| 249 | SAS Options: Versatile Players in the Game of SAS | Denise M. Poll | With hundreds of SAS options "in play" during a SAS session, keeping the players organized requires knowledge of: the position (performance, security, output control and so on) an option plays the timing of when the option can play (start-up, post-startup) the actions (get, set, reset) supported for an option This paper explains and provides example usage of interfaces available for establishing, updating, and querying SAS options. These are a sample of questions addressed in this paper: Which configuration file set an option value? How can an option value be reset to a previously used value, the default value, or the value established at SAS start-up? What is a restricted option and how can I tell if an option can be restricted or is restricted? | proc step, proc options, proc sql, sas log, options statement, dictionary tables, proc print, macro function | source | 8 |
| 250 | SAS Program Library: Benefits of Using the Confluence Wiki | Leonard Polak | SAS programmers often own or use programs that reside on different systems, maybe UNIX programs located in one storage location and Windows programs located in another storage location. Or SAS programs may reside on different file servers or in separate project folders. Bringing these programs together into one system can prove invaluable. And adding related documentation can add further value. One option to bring programs and documentation together is with a wiki. In essence, a wiki is a website that makes it easy for users to contribute pages and link them. The functionality and examples provided in this paper stem from Atlassian's Confluence wiki. SAS programs were copied to pages, Word documents were imported into pages, and PDF attachment links were added to pages. After discussing a SAS program library and the Confluence wiki, select benefits and features will be described. These benefits and features include program backup, version control, text formatting, search, and labels. | proc print, proc step | source | 12 |
| 251 | SAS Programming: Current State and Implications for the Future | John Gorden | Managing a programming department can be challenging. Day-to-day managers focus on getting/helping individual programmers to complete specific tasks within specific timelines. It can be helpful to step back from managing people to consider a broader perspective on the work that programmers do and the environment in which they do it. Using data from the US Department of Labor as a starting point, this paper presents a larger perspective on the programmer occupation, how it fits into organizations, how it is changing, and what managers and programmers should consider in planning for the future. | sas programmer, sas programming, dataset, character functions, sas base, sas enterprise guide, macro parameter | source | 6 |
| 252 | SAS Software Development with the V-Model | Andrew B. Ratcliffe | Software development is about building useful systems, not generating reams of documents. The V-Model helps the development team apply focus to what documents are useful (and why) and how much content is appropriate for each. The V-model offers a framework that clarifies the relationships between requirements, specifications, and testing. This paper discusses the benefits of the V-Model in a variety of differing development processes including waterfall and agile | macro variable, data cleaning, libname statement | source | 9 |
| 253 | SAS Web Report Studio: Exploring OLAP Data | Sherry Patillo, Rick Styll |
Did you know that you can explore OLAP data in SAS Web Report Studio? SAS Web Report Studio, the zero download query, analysis, and report tool included with the SAS Enterprise BI Server, allows access to data designed to serve your OLAP reporting needs. Capabilities such as direct OLAP access and ad hoc querying are described | missing value, bar chart | source | 25 |
| 254 | SAS WOW! How to Streamline Your SAS Programs by Shedding Lines and Adding Substance | Lisa A. Mendez, Lizette Alonzo |
Would you like to slim down your Base SAS programs by eliminating needless lines of code while adding substance? Join us for some real life examples that we came across when we were tasked to update our SAS programs. We'll show you how to "transpose" data when PROC TRANSPOSE doesn't seem to work, condense your input when inputting multiple files with the same file structure, and how to use macro variables wisely, not carelessly. All this can be yours by adding some sample code to your SAS toolbox. But wait, there's more! See the value of program headers and comment boxes, for no additional cost! That's right! All this can be yours just by reading this paper! Act now! |
macro variable, sample, txt, do loop, dataset, proc step, macro definition | source | 9 |
| 255 | SAS/ETS and the Nobel Prize | David A. Dickey | Techniques for which the 2003 Economics Nobel Prize was awarded are illustrated with examples. These are cointegration and autoregressive conditionally heteroscedastic models. For this second topic, an introductory example without the heteroscedasticity leads into the more complex scenario | unit root, proc step, proc autoreg, time series analysis, residual, proc arima, proc varmax, stationarity, regression analysis, sample, autoregressive model, likelihood function, moving average, confidence interval, maximum likelihood estimation | source | 14 |
| 256 | sasCommunity.org Benefits to JMP Coders | Charles E. Shipp, Kirk Paul Lafler |
The benefits of sasCommunity.org to SAS users are available to JMP users also, but participation has lagged. This is partly due to excellent JMP web sites including their discussion groups. Reasons for increased JMP participation on sasCommunity.org are il lustrated and discussed, including the interchange of data, statistical and graphics, between SAS and JMP software. The benefit of a community to have your work known and also to help newer users will become increasingly more important as JMP users support sasCommunity.org as its popularity grows | proc sgplot, data visualization | source | 9 |
| 257 | Scale Measurement: Comparing Factor Analysis and Variable Clustering | Albert Liau, Teck Kiang Tan, Angeline Khoo |
In the social sciences, f actor analytical methods are commonly used in the scale measurement in examining the structure of scales. This paper provides an alternative way of examining the structure of scale mea surement using variable cluster analysis. Three analytical methods are illustrated in this paper, namely exploratory factor analysis, confirmatory factor analysis and variable clustering analysis. As variable cluster analysis is rarely used and not widely discussed in the literature, the advantages, benefits and limitations of using this method are discussed. Comparison of variable clustering to the factor analytical approach is illustrated using the personal strength scale. PROC VARCLUS, PROC FACTOR, and P ROC CALIS are the three SAS procedures for these three analytical methods. The syntaxes of the three procedures are given and the results are interpreted and compared |
factor analysis, correlation, proc princomp, proc step, confirmatory factor analysis, factor loading, scree plot, proc factor, proc varclus, cluster analysis, r-squared, oblique rotation, covariance | source | 19 |
| 258 | Scenario and Stress Testing Using SAS/IML | Dmytro Korol, Dmitry Donin, Mykola Tkachenko |
The proposed paper presents a methodology for Scenario and Stress testing of a model based on defining and determining quantiles of the joint distribution of model's variables. The implementation of the methodology in the SAS R⃝ system gives a view on rarely used SAS R⃝ capabilities for dealing with quite sophisticated algorithms and uses Interactive Matrix Language (IML) available with SAS R⃝. The methodology can serve as a non-parametric method to determining extremes of a model, when the distribution of model variables is un- known. The technique we propose can be used in wide range of prob- lems related to the analysis of multivariate data |
stress testing, probability distribution | source | 12 |
| 259 | Seamless Dynamic Web Reporting with SAS | Donald L Penix Jr. | The SAS Business Intelligence platform provides a wide variety of reporting interfaces and capabilities through a suite of bundled components. SAS Enterprise Guide, SAS Web Report Studio, SAS Add-In for Microsoft Office, and SAS Information Delivery Portal all provide a means to help organizations create and deliver sophisticated analysis to their information consumers. However, businesses often struggle with the ability to easily and efficiently create and deploy these reports to the Web. If it is done, it is usually at the expense of giving up dynamic ad-hoc reporting capabilities in return for static output or possibly limited parameter-driven customization | bar chart, macro variable, sas enterprise guide, raw data, sas library, dataset | source | 15 |
| 260 | Search Phrases with Arrays and Macros: Public Health Death Surveillance | Amy E. Prestanski, Liva Nohre |
In recent years, real-time surveillance of data has become an important feature in public health preparedness and response. Surveillance systems for emergency room visits, over-the-counter drugs, and 911 calls have all been developed to detect potential acts of terrorism as well as every day outbreaks. Causes of death from death certificates provide important indicators of the health of a community. A typical death certificate will have the causes of death reported by physicians, entered by public health staff, and then coded to International Classification of Disease standards. The turnaround from the date of death to the date of coding can be weeks to months later. For real-time cause of death surveillance, the phrases written in the causes of death need to be analyzed. This paper provides a method that has been used by Maricopa County Department of Public Health for several years using SAS macros and arrays to conduct real-time surveillance of death certificates | proc step, proc tabulate, do loop, index, macro call, array, proc transpose | source | 9 |
| 261 | Sentiment Mining Using SAS Text Miner | Patricia B. Cerrito, John C. Cerrito |
In the past, attempts to capture sentiment in studies have used survey questions with Likert scales or qualitative analysis with a very small number of observations. With the use of text analysis provided in SAS Text Miner, it is now possible to use open-ended questions to analyze for sentiment. There are many terms in medical research that have different meanings to different players: patients, physicians, hospitals, payers, and government bureaucracies. With the Affordable Care Act (Obamacare), there will be additional accountability by providers as well as attempts to ration care based upon the definition of the terms, effectiveness, futility, necessary, and reasonable as well as the concept of quality of life. Such attempts are already occurring for the cancer drugs, Avastin and Provenge. Currently, quality of life surveys are based upon one concept developed largely by providers | data mining, cluster analysis | source | 17 |
| 262 | Setting Up a Different Windows Environment for Each SAS Project | Philip A. Wright, Alan K. Churchill |
Most users modify the autoxec.sas file in their ‗My SAS Files' directory and this suits them just fine. For awhile. As a SAS software user, chances are your employer will begin assigning you multiple projects simultaneously. If the source data for your projects are in different directories, different formats, or even on different servers with different authorizations, it could soon become quite confusing as to what is where and quite time consuming keeping everything straight, let alone initializing everything properly. Setting up a different SAS/WINDOWS environment for each project may be the answer |
cpu time, proc step | source | 8 |
| 263 | Single Sign-On Configuration and Troubleshooting for SAS 9.2 Enterprise BI Web Applications | Stuart J. Rogers, Heesun Park |
Single Sign-On (SSO) means different things for different configurations and applications. The Single Sign-On logic and process for SAS 9.2 Enterprise BI Web applications are examined based on various enterprise-level Web infrastructure configurations in which SAS Web applications are deployed. With such deployments, troubleshooting configuration and authentication errors is confusing and time-consuming. This paper explains Single Sign-On in terms of initial source of authentication, security arrangement, and trust relationships among the Web components and how SAS Web applications manage and consume externally authenticated user identity. Then this paper explores the troubleshooting strategies available for each of the enterprise-level Web infrastructure configurations. A methodology, along with recommended solutions, is presented for common error messages | sas log, error message, business intelligence, sample | source | 20 |
| 264 | Small Area Estimation for Survey Data Analysis Using SAS Software | Pushpal K. Mukhopadhyay, Allen McDowell |
Small area estimation is important in survey analysis when domain (subpopulation) sample sizes are too small to provide adequate precision for direct domain estimators. Popular techniques for small area estimation use implicit or explicit statistical models to indirectly estimate the small area parameters of interest. Indirect estimation requires you to go beyond the survey data analysis methods that are available in the SAS/STAT survey procedures. This paper describes the use of the MIXED, IML, and MCMC procedures to fit unit-level and area-level models, and to obtain small area predictions and the mean squared error of predictions. Hierarchical Bayes models are also discussed as extensions to the basic models |
covariance, proc mixed, sample, proc step, standard deviation, prior probability, regression analysis | source | 19 |
| 265 | So You're Still Not Using PROC REPORT. Why Not? | Ray Pass, Daphne E. Ewing |
Everyone who can spell SAS knows how to use PROC PRINT. Its primary use may be as a development tool to help in debugging a long multi-step program, or as a simple report generator when all that is really needed is a quick look at the data, or even a basic low-level finished report. However, if a report generation/information delivery tool with powerful formatting, summarizing and analysis features is called for, then PROC REPORT is the solution. PROC REPORT can provide the standard PROC PRINT functionality, but in addition, can easily perform many of the tasks that you would otherwise have to use the SORT, MEANS, FREQ and TABULATE procedures to accomplish. PROC REPORT is part of the Base SAS product, can run in both an interactive screen-painting mode or a batch mode, and should be the basic tool of choice when there is a need to produce powerful and productive reports from SAS data sets. This paper will present the basics of PROC REPORT (non-interactive mode) through a series of progressively more sophisticated examples of code and output. |
proc report, proc step, class variable, character variable, proc print, proc means, proc tabulate | source | 14 |
| 266 | So, That's What's in There? Customer Service through Unstructured Data | Bruce Bedford, Ben Zenick |
Business Intelligence (BI) and Analytical requirements have continued to change over the years as clients have recognized the power of analyzing and reporting on unstructured data. Unstructured data can come in a variety of different formats, customer comments and feedback, textual information, and dynamically changing source files, to name a few. This paper will focus on how organizations can leverage BI and Analytics to accurately identify and interpret trends buried deep in unstructured data. Through data consolidation and categorization it is possible to quickly pinpoint and resolve customer complaints that point to larger business issues. This enables organizations to get out in front of problems before they become crises. It also provides a continuous stream of real-time insights flowing from the front lines | unstructured data, business intelligence, sample, data cleaning, macro definition | source | 11 |
| 267 | So, You Want to Be a SAS Press Author! | Michael A. Raithel | This panel discussion will feature a group of SAS Press authors who will be talking about their experiences of writing and publishing SAS books. The discussion will be led by a moderator who will pose specific questions that will elicit information that prospective SAS authors will find interesting as they consider their own SAS book ideas. The point of this discussion is to offer SAS book authoring tips, techniques, and best practices to future authors of SAS Press books | sample, sas programming | source | 3 |
| 268 | Social Networking and SAS: Running PROCs on Your Facebook Friends | Susan J. Slaughter, Chris Hemedinger |
Social networking sites such as Facebook, LinkedIn, and Twitter offer tremendous opportunity for people to share information with each other. This information is simply data, organized in a friendly form for your use on the Web. It's also great raw material for interesting analysis in SAS - if you can get it there. This paper describes how to use SAS and SAS Enterprise Guide to extract data from social network sites using published APIs, with Facebook and Twitter as examples, and then perform basic analysis on the results | dataset, proc freq, proc step, missing value, sas enterprise guide | source | 8 |
| 269 | Some Tricks to Get the Best Out of SAS and UNIX | Chienchung Chen | After years of using SAS under varied platforms, I enjoyed working under UNIX environment since it provided me with some easy-to-learn commands when I needed to perform varied development, production, and analytical tasks. All those commands, if applied appropriately, can improve the efficiency of the codes and the usage of resource. Besides, depending on the objectives of the tasks, the developer can choose between batch and interactive SAS mode and integrate the SAS codes with UNIX scripts to reduce the run time of the jobs and avoid errors. Furthermore, with the help of UNIX, the developer can even automate some tasks which usually require manual steps under PC environment. This paper will provide some tricks mentioned above | macro definition, sample, excel | source | 6 |
| 270 | Special Missing Values for Character Fields | John F. Ladds | In SAS everyone knows about missing values for character and numeric variables and some people know about special missing values that allow you to have more than one meaning for a missing value on numeric variables. What about character variables? Can we mimic the same behaviour? This paper will look at special missing values and a technique to achieve the same results for character variables using non-printable hex codes | missing value, numeric variable | source | 8 |
| 271 | Stacking the Deck: Dynamically Filtering Prompt Values for Stored Processes | Susan Trelstad, Kyle Koenig, Alesia Pehl |
In self-service applications that access sensitive information, accurate data filtering cannot be left open to chance. Companies must fulfill legal obligations to restrict access to data or they might need to protect data to meet business requirements. Integrating security and other business rules to filter table-driven prompt values can be a significant challenge, especially if the source database does not accommodate row-level security. SAS 9.2 has new capabilities for presenting user-appropriate content in report-selection prompts. This paper offers two solutions for implementing dynamically filtered parameters: one for off-the-shelf SAS applications and the other for custom applications. The off-the-shelf solution uses SAS Information Maps to source dynamic prompts for SAS Stored Processes. The custom solution uses stored processes to populate parameter values in a Java Web application | data validation rules, sas base, sas enterprise guide | source | 9 |
| 272 | State of the Union: The Crossroads of CDISC Standards and SAS Supporting Role | Michael J. Molter, Chris Decker |
The Clinical Data Interchange Standards Consortium (CDISC) began in the late 90's with the goal of developing clinical research standards across the industry to optimize the drug development lifecycle and improve the regulatory review process . Over the last decade, CDISC has developed a myriad of models in the hope of reaching their goal. As with any new industry initiative, CDISC has gone through growing pains as the models have been met with mixed reviews by both industry and regulatory agencies. During this same timeframe, SAS has made a concerted effort to develop tools to support those same clinical research standards. This paper will discuss the current state of the CDISC standards describing the current use and adoption of the standards, an update on the individual CDISC models, and the challenges that lay ahead. In addition, the paper will describe the current SAS solutions that support CDISC standards and discuss the advantages and limitations of these solutions | clinical data, data cleaning, data standard, clinical research, clinical trial, sas programmer, sas base, cdisc | source | 14 |
| 273 | Statistical Modeling of Surface Complexation of Selenium onto Iron and Aluminum Oxides using SAS | Anpalaki J. Ragavan | Simulation of chemical element adsorption, release, and transport is possible without the need to perform time consuming, detailed adsorption studies on multiple samples by incorporating prediction equations into chemical speciation-transport models before simulating. Adsorption models also assume that the chemical species in a mixture is at chemical equilibrium which is the state in which the chemical activities or concentrations of the reactants and products have no net change over time. By the second law of thermodynamics, a mixture of chemicals satisfies its chemical equilibrium state (at a constant temperature and pressure) when the free energy of the mixture is reduced to a minimum. In this study a constant capacitance chemical surface complexation model was applied to simulate selenate (Se(IV)) adsorption on iron and aluminum oxides by optimizing monodentate Se(IV) surface complexation constants and surface protonation constants | proc step, sample, proc mixed | source | 13 |
| 274 | Storage Space and Processing Time Comparisons between Horizontal and Vertical Longitudinal Data | John C. Hennessey | A longitudinal, horizontal person-record flat file, with 7,152 variables, is converted to a SAS library of 15 datasets. One of the datasets contains the static variables. The other fourteen datasets are vertical longitudinal event history datasets for each of the longitudinal variables. The size of the resulting SAS library is 18% of the size of the original flat file. Further, the processing times to answer standard longitudinal questions are significantly shorter than the time to generate the same results from the horizontal file, even starting as a SAS dataset. Enterprise Guide and custom code are used to convert the horizontal file to vertical event history files and back to horizontal files | sas enterprise guide, flat file, cpu time, dataset | source | 10 |
| 275 | Studying Missing Data Patterns Using a SAS Macro | Theresa Schwartz, Qixuan Chen, Naihua Duan |
Before diving into the analysis phase of any study, it is essential to examine the data thoroughly. Performing univariate analyses is a good first step, noting not only what data are observed, but just as important, what data are missing. This is especially important when working with complex survey data involving skip patterns. It is useful to check missing data patterns to examine whether the survey was administered and entered correctly. Missing data patterns can also assist in variable selection for statistical analyses as well as missing-data imputation. We present here a SAS 9.2 macro, %missingPattern, that makes identifying missing data patterns fast and easy. The macro is designed to look at missing data in four ways: the proportion of subjects with each pattern of missing data, the number and percentage of missing data for each individual variable, the concordance of missingness in any pair of variables, and possible unit nonresponse. The user can customize these analyses by specifying which variables to include or exclude, and which output should be produced. We illustrate the use of our macro with a simple hypothetical example. | missing value, dataset | source | 4 |
| 276 | Subsetting Large Data Sets Created via the Complex WHERE Clause | Mikhail Gruzdev, Allen J. Blackburn |
SAS offers several methods to enable fast data extraction from large data sets. The discussion, here, is of techniques offered within base SAS as extraction options for the minimization of overall processing times. Our paper points rather positively to the efficient usage of the complex "WHERE" clause coupled with its complex "OR" logic options. Our paper then goes through several iterations of advanced processing techniques including "SORT" and "INDEX" then favorably compounds total processing efficiency by use of the "POINT=" option with utilization of the SAS observation number. By combining these techniques, access speed is increased, at minimum, a factor of five or six times. A rudimentary table explains overall efficiency | cpu time, point= option, dataset | source | 5 |
| 277 | Supercharged Analytics 2011: Leveraging SAS and the Power of Database Appliances | Rick Miller | Presentation | source | 21 | |
| 278 | Surfacing Content from SAS Enterprise Business Intelligence in Third-Party Portals | Meera Venkataramani, Gaurav K. Agrawal, Sam Atassi |
Corporate portals have become the preferred choice for many enterprises to surface their company information. These portals provide a single Web site for all users to access and discover services and content provided by the enterprise. This paper focuses on integrating SAS content, such as reports, stored processes, and dashboards in an IBM WebSphere Portal. It presents a customer-use case with examples for configuring, deploying, and customizing content using JSR 168 SAS BI portlets. The paper shows how to personalize the portlets to share a pre-populated content view to a group of users in the enterprise. The paper explores methods to surface SAS content in Microsoft SharePoint | access control, sample | source | 15 |
| 279 | Taking Disease and Health Management Analytics into the Next Generation | George S. Habek | Do you struggle to visualize results of predictive modeling and segmentation within your healthcare member network? Would you like to leverage new opportunities within specific patient/member segments? A streamlined data mining approach that uses PROC GTILE to answer these questions has arrived. This approach combines the results of a segmentation process through cluster analysis for the patient/member population with predictive model results formed through decision trees and visualized through interactive tree maps. This document outlines a five-phase best-practice strategy for conducting disease management analytics: 1. Data preparation 2. Segmentation analysis 3. Predictive modeling 4. Linking of segmentation analysis and predictive modeling 5. Use of PROC GTILE to visualize results of Phase 4 via tree maps |
data mining, cluster analysis, proc hpsplit, sample, dataset, likelihood function, type ii error, type i error, proc means, proc step, regression analysis, logistic regression, unsupervised learning, feature importance, proc freq, logistic distribution, nominal variable | source | 13 |
| 280 | Taking PROC SUMMARY a Step Beyond | Priya Suresh | PROC SUMMARY can be used to display cross-tabs of multiple variables to quickly identify unique patterns in the data. The summary output shows the unique pattern of input variables and the resultant value of the derived variable in an easily verifiable format. If the same code is applied to data over multiple time periods, the same patterns are repeated again and again. The next step beyond PROC SUMMARY is to use exact pattern matching technique to weed out the verified patterns from prior time periods and display only the new patterns that occur in the data. This presentation describes the application of this technique and explains how SAS is used to provide an elegant solution |
proc step, proc means, proc export, proc import, dataset, input statement, proc print | source | 7 |
| 281 | Taking Your Analytics Up a Notch by Integrating Clickstream Data | Bill Franks | As technology progresses, analytics that were theoretically exciting, but not pragmatically feasible, become achievable. One such area is a fuller integration of detailed, customer-level behavioral data sourced from a Web site into an enterprise analytics environment. Most companies stop their Web integration with the inclusion of online sales transactions. Web vendors provide operational reporting on click-thru rates, traffic sources, and Web-centric metrics based only on Web data. However, the detailed Web behavior data is not usually leveraged outside of Web reporting. Leading companies have shown how that detail can provide previously untapped corporate value. This presentation will outline what those leaders are doing, why they are doing it, and why your organization should consider such actions today. The examples are quite compelling and promise to be eye opening to those who have not yet given much thought to integrating their detailed clickstream data | credit card, web analytics, proc means | source | 10 |
| 282 | Taxonomies, Tone, and Tweets: Where Text Analytics and Social Marketing Collide | John J. Bastone | Text analytics is an established science. Social marketing has exploded in popularity. The latter phenomenon has generated an enormous amount of interest in the former. As the software landscape is quickly adapting to the needs of next year's business, there are some core issues that companies must wrestle with in embarking on any social media analytics engagement: Listening - By growing bigger ears to hear beyond the usual sites, to pull in relevant conversations about your business. Processing - To ensure your organization understands what it is hearing. * Responding - Both literally and procedurally, to ensure the organization adapts and learns from direct customer feedback | source | 9 | |
| 283 | Ten Ways to Improve the Efficiency of Clinical Statistical Programming | Amos Shu | The competition among Clinical Research Organizations (CROs) is extremely fierce. Improving programming efficiency is a way to lower cost in the clinical statistical analysis area. This paper discusses ten ways to improve the programming efficiency: 1) avoid unnecessary sorts, 2) use sortseq = UCA (numeric_collation =on) option, 3) use criterion option in PROC COMPARE, 4) use compute block to add more than 10 footnotes, 5) use alias method of columns in PROC REPORT to create wide reports, 6) find the first and last day of the same month, 7) use picture statement in PROC FORMAT to align decimal places, 8) add alphabet letters to easily sort statistics, 9) use dummy datasets to make up tables that do not have data available, 10) create treatment "total" |
proc step, proc report, proc transpose, proc compare, proc format, proc freq, proc sql, proc means, clinical trial | source | 5 |
| 284 | Teradata for the SAS Programmer: Ordered Analytical Functions, Hash Objects, ANSI SQL: 2003 | Richard Andrews, Jignesh Miyani |
The manner and speed in which information is hashed and indexed makes Teradata a valuable tool for satisfying time sensitive Congressional requests at the Centers for Medicare and Medicaid Services (CMS). Ordered Analytical Functions enable On-Line Analytical Processing (OLAP) requests containing ANSI SQL: 2003 window functions, which greatly enhance the functionality of the Structured Query Language (SQL). This paper will discuss how SAS 9.1 programming techniques can be imitated within the Teradata 12 environment using SAS\ACCESS pass- though queries and the simulation of By-Group processing with dot notation. The methods discussed will help users make an easier transition from traditional SAS programming to a Massively Parallel Processing (MPP) Teradata platform running shared nothing architecture | proc sql, order by, windowing, ansi sql, group by, in-database processing, parallel processing, data mining, hash table, by group processing, format statement, proc step | source | 11 |
| 285 | Teradata Parallel Transporter: Loading Your SAS Data Just Became Easier | Jeffrey Bailey | Teradata Parallel Transporter (TPT) provides a full-featured application programming interface (API) that enables you to access the Teradata loading feature. If you use SAS with Teradata, you need to know about this exciting technology. This paper will help you start using the SAS/ACCESS Interface to Teradata and Teradata Parallel Transporter to efficiently move SAS data to a Teradata Server. Although very important, restarting load jobs is beyond the scope of this paper and is not fully covered. It is mentioned where required. The good news is that this paper will give you the head start that you need to learn how to restart jobs on your own |
proc append, proc step, sas log, proc sql, create table, sample, cpu time, drop table, relational database | source | 16 |
| 286 | Testing the Adequacy of ARMA Models using a Weighted Portmanteau Test on the Residual Autocorrelations | Thomas J. Fisher | In examining the adequacy of a statistical model, an analysis of the residuals is often performed. This includes anything from performing a residual analysis in a simple linear regression to utilizing one of the portmanteau tests in time-series analysis. When modeling an autoregressive-moving average time series we typically use the Ljung-Box statistic on the residuals to see if our fitted model is adequate. In this paper we introduce two new statistics that are weighted variations of the common Ljung-Box and, the less-common, Monti statistics. A brief simulation study demonstrates that the new statistics are more powerful than the commonly used Ljung-Box statistic. The new statistics are easy to implement in SAS and source code is provided | partial autocorrelation, time series analysis, moving average, null hypothesis, sample, regression analysis | source | 5 |
| 287 | The Anti-Curse: Creating an Extension to SAS Enterprise Miner Using PROC ARBORETUM | Andrew S. Cathie | SAS Enterprise Miner introduced a toolkit to enable programmers to create their own nodes in SAS Enterprise Miner 5.3. These nodes can then be used in the same manner as regular SAS Enterprise Miner nodes under the Sample, Explore, Modify, Model, Assess, or Utility menus. The user -created nodes make use of the flow features and metadata in SAS Enterprise Miner and allow you to implement your own algorithms, perform special -purpose data manipulation or reporting, and so on. This paper covers my experience of developing my own node, the nominal to interval node, which converts high -cardinality nominal inputs (ZIP codes, for example) to lower -dimensional interval inputs using decision trees. The paper goes some way toward addressing a persistent data mining problem, the curse of dimensionality. During this I describe some of the tools avai lable, hints, and tips, and how the finished result looks. Code is available from the author | data mining, proc hpsplit, nominal variable, response variable, proc step, macro variable, macro put, interval variable, sample, regression analysis, score code | source | 11 |
| 288 | The Day the Server Said Tweet! | William W. Viergever, Daniel Kuiper, Koen Vyverman |
It was an idyllic summer afternoon at the SAS Netherlands Technical Support call center. The pervasive hum and buzz of problem-solving activity was punctuated by the shrill cries of woodland birds drifting in through the open windows. Then, a dormant screen sprang to life, and panicky error messages and warnings started scrolling by! A distant siren wailed. Support staff gathered swiftly around the screen. It was the MIS server tweeting messages of impending doom! A SAS Portal guru sat down at a console, logged on to the server, diagnosed, and fixed the problem. Not five minutes later, the screen went black again and relative serenity returned. The Managing Director had not even noticed the portal's hiccups. This paper shows how to use Twitter to let your mission-critical SAS systems tweet their warnings and errors straight to your local support team for immediate follow up, and thus helps to minimize downtime! | sas batch, work library | source | 18 |
| 289 | The Essence of DATA Step Programming | Arthur X. Li | The fundamental of SAS programming is DATA STEP programming. The essence of DATA STEP programming is to understand how SAS processes the data during the compilation and execution phases. In this paper, you will be exposed to what happens "behind the scenes" while creating a SAS dataset. You will learn how a new dataset is created, one observation at a time, from either a raw text file or an existing SAS dataset, to the program data vector (PDV) and from the PDV to the newly-created SAS dataset. Once you fully understand DATA STEP processing, learning the SUM and RETAIN statements will become easier to grasp. Relating to this topic, this paper will also cover BY-group processing |
output, dataset, automatic variable, retain, execution phase, input statement, _n_, compilation phase, if-then-else, out=, by group processing, sas log, set statement, in=, subsetting if, infile statement, missing value, put statement, proc step | source | 19 |
| 290 | The Final Frontier: A SAS Approach to Data Envelopment Analysis | Sabah Sadiq | The purpose of this paper is to discuss the methodology for using PROC OPTMODEL and macros to rank real estate properties using Data Envelopment Analysis (DEA) and Super Efficiency Models. DEA was used to categorize apartments as efficient or inefficient, in terms of costs and benefits as seen through the buyer's perspective. The Super Efficiency Model was then used to comparatively rank efficient apartments in order of cost effectiveness. During this process, macros using OPTMODEL index sets and arrays were developed that will compute DEA and Super Efficiency values given data sets containing input and output values. These macros allow SAS/OR users to seamlessly implement DEA while greatly reducing modeling time and coding errors. PROC APPEND was also included in the macro in order to accumulate efficiency values and produce a graphical comparison of efficiencies |
proc step, odds ratio | source | 7 |
| 291 | The Greatest Hits: ODS Essentials Every User Should Know | Cynthia L. Zender | Just when you think you know every song (feature) in the ODS hit parade, you discover that there's an option or destination or feature that has you singing its praises because the feature boosted your reports to the next level. This paper covers some of the essential features and options of ODS that every user needs to know to be productive. This paper shows concrete code examples of the ODS "Greatest Hits". Come to this session and learn some of the essential reasons why ODS and Base SAS rock! |
ods html, excel, proc step, proc tabulate, proc print, proc report, sas log, output delivery system, proc reg, ods pdf, file statement, proc template, dataset, output, proc univariate, ods output, ods csv, ods rtf, anova, by group processing, estimate, select, txt, user-defined format, sas output | source zip |
34 |
| 292 | The Little Engine That Could: Using LIBNAME Engine Options to Enhance Data Transfers Between SAS and Microsoft Excel Files | William E. Benjamin Jr. | Many people are not aware that the SAS Access for PC Files product will allow SAS Programmers to access an EXCEL spreadsheet in much the same way as any other SAS file. There are of course some restrictions, but there are also a lot of options that help remove some of the bumps in the road. The LIBNAME statement allows the user to define an EXCEL file in SAS terms and gives the programmer access to LIBNAME and data set options to control how the EXCEL file is defined, accessed, and yes even how the data will be formatted. This paper will describe some of those options |
excel, variable, libname statement, sas library, proc sql, proc step, dataset, character variable, libname engine, numeric variable, sysdate9, sas programmer, missing value | source | 10 |
| 293 | The Many Ways to Effectively Utilize Array Processing | Arthur X. Li | Utilizing array processing allows you to reduce the amount of coding in the DATA STEP. Some may find that using array processing is confusing or even intimidating. First of all, the syntax for constructing arrays, especially for multi- dimensional arrays, is complicated; there are way too many rules. Once mastering syntax rules, you still need to understand what happens in the Program Data Vector (PDV) during array processing; otherwise, the data set that you created by using array processing may not be the one that you intended. Lastly, knowing when best to use array processing is another challenge. In this section, in addition to learning how to create one- and multi-dimensional arrays, you will also receive a brief review on how to create an explicit loop in the DATA STEP - the prerequisite for constructing an array. You will also be exposed to what happens in the PDV during array processing |
array, do loop, execution phase, output, in=, out=, dataset, compilation phase, array bounds, missing value, automatic variable, implicit loop, temporary array, set statement, class variable | source | 14 |
| 294 | The Perfect Marriage: The SAS Output Delivery System (ODS) and Microsoft Office | Chevell T. Parker | A compatible relationship is one in which the associated entities complement each other's strengths and compensate for each other's weaknesses. This relationship is certainly the case when you combine the power of ODS and the flexibility of Microsoft Office. This paper describes how you can create outstanding spreadsheets via the harmonious union of ODS with Microsoft Office applications. The paper explores the relationships among ODS destinations (for example, ExcelXP and MSOffice2K) as well as custom TAGSETS (for example, the TableEditor tagset). In addition, the paper explains how these components enable you to generate highly presentational spreadsheets |
excel, output delivery system, proc step, sample, proc template, mod, ods tagsets.excelxp, proc report | source | 22 |
| 295 | The Power to Plot: Three Ways to Enhance SG Graphical Output | Xiangxiang Meng | In SAS 9.2, a set of statistical graphics (SG) procedures , including the SGPLOT, SGPANEL, SGCATTER, and SGRENDER procedures, are introduced to create high-q uality template-based graphs using the Graph Template Language (GTL). This paper discusses three different ways to enhance the statistical gr aphics output generated by SG procedures. The cooperation of SG procedures, ODS styles and GTL language is the focus of this paper, and the TEMPLATE procedure is highly involved to control and modify ODS style or GTL program |
proc step, proc template, proc sgpanel, proc sgplot, bar chart, scatter plot | source | 12 |
| 296 | The RANDOM Statement and More: Moving On with PROC MCMC | Fang K. Chen | The MCMC procedure, first released in SAS/STAT 9.2, provides a flexible environment for fitting a wide range of Bayesian statistical models. Key enhancements in SAS/STAT 9.22 and 9.3 offer additional functionality and improved performance. The RANDOM statement provides a convenient way to specify linear and nonlinear random-effects models along with substantially improved performance. The MCMC procedure also supports multivariate distributions, such as the multivariate normal and inverse-Wishart distributions, and implements conjugate sampling methods when appropriate to improve sampling speed. This paper describes key enhancements of PROC MCMC in SAS/STAT 9.22 and 9.3 and illustrates the use of these enhancements with examples |
proc step, prior probability, proc mixed, sample, likelihood function, regression analysis, logistic distribution, logistic regression, covariance | source | 21 |
| 297 | The SAS Hash Object: It's Time To .find() Your Way Around | Peter W. Eberhardt | "This is the way I have always done it and it works fine for me." Have you heard yourself or others say this when someone suggests a new technique to help solve a problem? Most of us have a set of tricks and techniques from which we draw when starting a new project. Over time we might overlook newer techniques because our old toolkit works just fine. Sometimes we actively avoid new techniques because our initial foray leaves us daunted by the steep learning curve to mastery. For me, the PRX functions and the SAS hash object fell into this category. In this workshop, we address possible objections to learning to use the SAS hash object. We start with the fundamentals of the setting up the hash object and work through a variety of practical examples to help you master this powerful technique | hash table, dataset, lookup, proc sql, hash iterator, out=, sysrc, sas programmer, sample, data merging, proc step | source | 17 |
| 298 | The Top 10 Head Scratchers: SAS Log Messages That Prompt a Call to SAS Technical Support | Kim M. Wilson | As a DATA STEP programmer, you know the sigh of relief that comes when your job finishes and your SAS log is clear of any errors or warnings. When these messages do occur, most of the time they are intuitive enough so that you can move directly to the offending code, make the necessary changes, and complete the job successfully. However, what do you do about those perplexing messages that sometimes appear, making you scratch your head in puzzlement? This paper examines the top 10 errors, notes, and warnings that prompt DATA STEP programmers to call SAS Technical Support. The discussion addresses the common causes of the messages and provides solutions so that you can reduce your troubleshooting time and effort |
character variable, options statement, sample, input statement, infile statement, merge statement, sas log, error message, dataset, length statement, numeric variable, put statement, by group processing, user-defined format, if-then-else, proc print, proc step | source | 9 |
| 299 | The Trilogy on e-Mailing, Part 3: Handling e-Mail Attachments with SAS | Erik W. Tilanus | This is part three of the trilogy on e-mailing and explains how to handle attachments in incoming e-mails with SAS. There are two types of attachments: text-based attachments (plain text files, HTML) and binary attachments (anything else; e.g., Microsoft Office documents, pictures). Binary attachments are normally encoded to make them compatible with the text-based character of the e-mail system. The presentation starts with recognizing attachments in the e-mail data stream. But the main topic is how to decode BASE64-encoded binary attachments and file the resulting binary file. Part one of the trilogy handled sending e-mails from the DATA STEP and was presented at SAS Global Forum 2008. Part two handled receiving e-mails in a DATA STEP; i.e., using the DATA STEP as a POP3 mail client and was presented at SAS Global Forum 2009 |
txt, put statement | source | 5 |
| 300 | The Validator: A Macro to Validate Parameters | Steven A. Wilson | When developing reusable macro tools, one important aspect of coding such macros is how to validate the parameter values passed to the macro. While all developers should strive to include some validation of passed parameter values, the type of validation, messages returned and exception handling should be performed in a consistent fashion for all macros in your autocall library. This paper discusses the development of a macro that is a utility for validating parameter values passed into other macros. This allows a consistent set of parameter validations to be performed with consistent results within your macro toolbox. Techniques for using this utility macro as a stand-alone tool as well as implementation as a stored- compiled macro are also discussed | data cleaning, macro conditional, autocall macro, macro variable, macro definition, sas log, stored macro, macro loop, exception handling, options statement, automatic macro variable, macro facility, where statement, macro flow control, proc sql | source | 13 |
| 301 | This SAS Program Says to Google, "What's Up Doc?" | Scott Davis | When you think of the internet, there are few things as ubiquitous as Google. What may not be quite as well known is Google docs which is a web-based Office-like suite for document creation and management. You can create and manage a number of different documents and store them for free on the internet. There is a tremendous advantage to using a tool like Google docs for the consumer and for the small business owner alike. That advantage, of course, is the cost. Google docs = FREE. Where does SAS come into play? SAS has the tools (with a little finessing) to retrieve data from one of these documents and turn it into information. This paper explores using the FILENAME URL statement to retrieve data from a Google docs spreadsheet. The data can then be manipulated, turned into a report and published back out to the internet for your colleagues. |
filename statement, data cleaning, date variable, mod, ods html, raw data | source | 6 |
| 302 | Those Confounded Interactions: Building and Interpreting a Model with Many Potential Confounders and Interactions | David J. Pasta | When constructing a general (or generalized) linear model with many possible covariates, it is often important to consider very carefully the role of potential confounders and interactions. Once a model is constructed, the interpretation of the coefficients depends on which other terms are included in the model. It is especially important to interpret interactions carefully if the model does not include all lower-order interactions and main effects. Depending on the context, you may want to set a low threshold for statistical significance for interactions (including them even if they are of borderline significance) or a high threshold (including them only if they are highly significant). Some variables you may want to treat as concomitant, including them regardless of statistical significance. Important confounders may fall into this category. This paper provides practical suggestions for building and interpreting models with interactions | estimate, main effect, interval variable, least squares mean, proc genmod, response variable, degrees of freedom, proc step, predictor variable | source | 13 |
| 303 | Tick-Tock, Watch the Clock: How Long Will This Take to Run? (or Making SAS Information Maps and SAS Web Report Studio Projects More Efficient) | Fazal Davood, Susan M. Jumper |
Eager to use the new tools we'd been given, we quickly realized that carefully preparing our data for use in SAS Information Maps and Web Report Studio was the key to maximizing efficiency and minimizing frustration. In an attempt to satisfy both our SAS administrators and our customers, our goal was to provide the most flexibility that Information Maps and Web Report Studio had to offer with the caveat that the project also return results rapidly. Compiling our source data to create effective Information Maps that take full advantage of the filters available, and subsequently, using the prompt capabilities in Web Report Studio while reducing run-time required forethought. In this paper, we will look at an example of one particular set of client reports. Our objective will be to outline the performance concerns, and follow up with the approach taken to tackle the issues | raw data, cpu time | source | 12 |
| 304 | Time Series Data Mining with SAS Enterprise Miner | Sascha Schubert, Taiyeong Lee |
Traditionally, data mining and time series analysis have been seen as separate approaches to analyzing enterprise data. However, much of the data generated by business processes is time-stamped. Time series data mining is a marriage of forecasting and traditional data mining techniques that uses time dimensions and predictive analytics to to make better business decisions. SAS has developed a collection of techniques that will be integrated into SAS Enterprise Miner 7.1 . This paper introduces these new time series data mining techniques | time series analysis, time series data, data mining, proc esm, outlier, confidence interval, data wrangling, missing value, hierarchical clustering, proc step, imputation | source | 12 |
| 305 | Time-Critical Concise Reporting in the Age of Twitter and Wireless Mobility: Continuous Real-Time Monitoring, Sparse Line Charts, and Compact Tables | LeRoy Bessler | According to its web site, "Twitter is without a doubt the best way to share and discover what is happening right now." This paper will show you that SAS is an excellent way to discover and share what is happening right now. You can keep in touch with your data by creating a program to continuously monitor critical values and, when appropriate, send email alerts to people with a need to know. The email can optionally include a link to, attach, or imbed a tabular or graphic report. What's happening? Are we up? Are we down? What direction are we going? I presented my sparse line chart concept in 1991, long before the "spark line" emerged. I have long claimed that usually the only important aspects of a trend, besides current direction, are where it started, where it is now, and its prior high and low, if any. The paper includes several sparse line charts, some developed for the BlackBerry, iPhone, etc | macro loop, macro conditional, macro definition, proc step, line chart, horizontal bar chart, bar chart, if-then-else, proc gplot, proc sql | source | 20 |
| 306 | Tips and Techniques for Automating the SAS Add-In for Microsoft Office with Visual Basic for Applications | Tim Beese | Do you want to run SAS Stored Processes in Microsoft EXCEL and set the prompt values from your worksheet? Do you want to filter your SAS data sets based on the values of specific cells? Do you want to customize your Microsoft Office content with buttons and other controls that allow you to open data, run SAS code and refresh results? This is now possible! The SAS Add-In 4.3 for Microsoft Office adds many new features that allow users to interact with their content through Visual Basic for Applications (VBA). Users can insert and refresh data, stored processes, and reports using VBA. It is also possible to provide prompt values to stored processes, filter and sort strings for data, and control where results are displayed. Combining these features with the existing functionality provided by VBA in Microsoft Office, integration of SAS within Microsoft Office will become easier and more powerful! |
excel, xls, sample, ods excel | source | 15 |
| 307 | Tips and Techniques for SAS Programmers | Helen D. Carey, Ginger Carey |
If you are new to programming or even are an experienced programmer, you will benefit by learning tips and techniques that will help you be more productive. This presentation offers tips in programming, efficiency, work habits, and where to find answers to your SAS questions. These tips come from our own experience and from learning from others through their presentations and papers | proc step, proc sql, sas enterprise guide, dataset, output delivery system, macro variable, proc format, proc freq, macro facility, user-defined format, sample, proc tabulate, sas programmer, missing value, proc sort, proc univariate, dictionary tables, macro function, proc transpose, excel, macro definition | source | 19 |
| 308 | Tips and Tricks for Clinical Graphs using ODS Graphics | Sanjay N. Matange | Did you know that you can create an adverse event graph using a vector plot? Or, that you can label dosage levels for a medications plot using a scatter plot? How do you place a reference line between two values on a category axis? S tatistical graphics (SG) procedures and the graph template language (GTL) provide you myriad ways to mix and match statements to create graphs. What you can achieve is based on creative usage of the statements. This presentation includes tips and tricks you can use in SG procedures and GTL programs to build your graphs. We use examples from clinical trials and health and life sciences domains to illustrate the techniques using real - world graphs like LFT panels, patient profiles, adverse event plots, and more. Most examples use the second maintenance release of SAS 9.2, but this presentation also includes a sneak preview of some powerful new features to be released with SAS 9.3 | scatter plot, box plot, proc sgplot, adverse events, forest plot | source | 16 |
| 309 | To FREQ, Perchance to MEANS | Christopher J. Bost | Should you run PROC FREQ or PROC MEANS on a variable? That is the question. This paper shows how to use the PROC FREQ option NLEVELS to determine the number of levels of each variable and then, based on a user- supplied cutoff, run either PROC FREQ or PROC MEANS. The Output Delivery System (ODS) and macro variables created with PROC SQL are used to automate the process |
proc step, proc freq, dataset, macro variable, proc sql, proc means, output delivery system, where statement, nominal variable, interval variable, character variable, proc print, select, numeric variable, create table | source | 10 |
| 310 | To Hash or Not To Hash! That Is the Question!! When it Comes to SQL!!! | Mustaq Ahmad | This paper demonstrates how to compare the efficiency of using the hash object when the same results can be produced with PROC SQL. In the process of efficiency quantification, other efficiency determinants such as data set size, compression, index, and environmental factors (remote PC versus local laptop) are accounted for in an experimental model with a simulation approach |
proc step, proc sql, hash table, cpu time, interaction effect, response variable, predictor variable, proc genmod, proc glm, feature selection, main effect, data science, lookup, proc reg, regression analysis, adjusted r-squared, proc glmselect, sas log | source | 12 |
| 311 | Top Ten SAS Sites for Programmers: A Review | Charles E. Shipp, Kirk Paul Lafler |
We review the top ten SAS sites for coders, beginning with sas.com and jmp.com. We then expand to sasCommunity.org, support.sas.com, and six other popular sites that assist you in training and programming. If you use Google to search for SAS Web sites, you will get over a million hits. We narrow the list down to ten | bar chart, sas programmer | source | 5 |
| 312 | Traffic Lighting Your Reports the Easy Way with the SAS Output Delivery System and PROC REPORT | Andrew H. Karp | Effective combination of core tools in BASE SAS Software enables you to create effective and compelling reports of data that "traffic-light," or conditionally assign appearance attributes, to specific elements of the output as a function of rules that are either "outside the data set" (e.g., an industry standard or corporate policy/designation) or "inside the data set" (e.g., those data values that are above the average (mean) of all the values in a column are to be displayed in one color, and those below the average are to be displayed in a different color) | proc report, proc step, sas base, output delivery system, macro facility, proc print, symbol table, proc format, macro definition, macro variable, sas output, format statement, ods pdf, quality control, data step | source | 12 |
| 313 | Troubleshooting Tips and Techniques for a SAS 9.2 Business Intelligence Environment | Bubba Talley, Tanya Kalich |
As a SAS 9.2 administrator, you are challenged every day with managing the health of your SAS Business Intelligence (BI) environment. Knowing the underlying architecture and its tools is the key to keeping your system running smoothly and diagnosing problems quickly. This paper provides an overview as well as specific examples to address these questions: How do your SAS 9.2 servers interact with your SAS BI clients? What tools are available to help monitor and diagnose your system? How do you find the information that will help you most when you investigate problems? How do you use that information to pinpoint the source of the problem? * What can you do to mitigate the effects of any problems that do occur? This paper is most useful to SAS administrators who have questions from their own experience maintaining a SAS Business Intelligence environment | business intelligence, sas management console | source | 21 |
| 314 | Tunneling Your Way to a More Secure SAS/CONNECT | Greg J. McLean | For many years now SAS/CONNECT software has allowed SAS users to make use of remote servers for data processing. However, in our modern day processing environments, security has become a very important issue that cannot be neglected. Within the SAS/CONNECT environment we have several encryption technologies available that will allow us to communicate securely between a client SAS session and a remote SAS session. Choosing the appropriate encryption technology will depend on the level of security that is required. Many of the common encryption algorithms that are utilized include RC2, RC4 DES, TripleDES and AES. The purpose of this paper is to explore the use of a particular encryption technology referred to as Secure Shell (SSH) Tunneling within the SAS/CONNECT environment. One of the great advantages of SSH is its flexibility and ease of use. SSH can be obtained through several commercial vendors as well as freeware (such as OpenSSH) |
proc step, sas log | source | 10 |
| 315 | Turbocharging Your Microsoft Office Environment with SAS Add-In for Microsoft Office 4.3 | Anand V. Chitale | Some of the most commonly used tools by users of all skill levels across organizations are Microsoft Office tools, such as Word, EXCEL, and PowerPoint. The SAS Add-In for Microsoft Office turbocharges these tools, giving users the power to explore and analyze information using SAS software within their familiar Microsoft Office environment. The SAS integration with Microsoft Office brings a large set of capabilities enabling users to access reports, stored processes, and dashboards; carry out multidimensional OLAP analysis in spreadsheets with auto-charting; run interactive reports for quick what-if analysis; and securely work with data directly. It brings capabilities allowing users to reutilize the work done, thereby increasing user productivity. This paper highlights the key capabilities provided by the SAS Add-In 4.3 for Microsoft Office and the application of these capabilities across the Microsoft Office products |
excel, business intelligence, sas enterprise guide, data science, sensitivity analysis, dataset | source | 21 |
| 316 | Turn Web Source Code into SAS Data in Two Easy Steps | Ryan Massa-McKinley | In the ever-expanding World Wide Web, there are millions of new websites every month with potentially valuable information. Many SAS users would like to convert source code from a url or list of url's into SAS data. SAS offers tools for web crawling and text mining to turn the mounds of textual data into information. This paper will provide the readers with an easy way to extract the text from websites into SAS data sets using Base SAS in just two data steps | data step, index, macro quoting function, tranwrd function, sas base, character variable | source | 4 |
| 317 | Understanding Drivers of Achieving Academic Success in University Students | David Dobson | An in depth survey was conducted to understand the drivers for academic success in university students. Over 400 undergraduate students, mostly majoring in business, participated in an online survey at the beginning of the fall 2010 semester. The survey questionnaire consisted of both closed-ended (structured) and open-ended (unstructured) questions. The combining of both structured and unstructured data gave a better understanding of the factors which impact academic performance. This paper demonstrates the application of both the data mining and text mining techniques that were used | correlation, data mining, proc freq | source | 17 |
| 318 | Understanding the Anatomy of a SAS Deployment: What's in My Server Soup? | Mark L. Schneider, Donna Bennett, Connie Robison |
Did you want to better understand the pieces of a SAS metadata-based deployment and where they go and what they do? This paper highlights the major components of a metadata-based SAS deployment of solutions and business intelligence. Using the concepts of topology and tiers as a framework, this paper illustrates the location of key components and how they can be distributed across machines | sas base, business intelligence, data mining, sample, sas enterprise guide, proc sql, dataset | source | 17 |
| 319 | Unit Cost Accounting at Allianz Germany - Live Demo of SAS Financial Management | Stephan Schnurr, Andreas Bölscher, Wolfgang Aleker |
Allianz Germany is the largest insurance company in Germany. For the Operations division (which has more than 9,000 employees), a unit cost accounting application was developed using SAS Financial Management. Unit cost accounting at Allianz is used as an element of business control and source of feedback to the Property & Casualty and Life and Health segments. The mission of the project has been: - Calculate costs per transaction and costs per claim or policy - On smallest possible unit (skill group) - Possibility of bridging to SAP Cost Accounting - Possibility to report different levels of details for costs and volume - Monthly availability - Include data and structure history | excel, logistic distribution | source | 9 |
| 320 | Unleashing the Power Behind Stored Processes | Ian Amaranayake | Managing SAS code within an organization can be challenging, especially when trying to control access to most recent versions of SAS programs. Stored processes provide us with an enterprise wide solution to this problem, enabling us to distribute the latest versions of programs for use in a variety of SAS 9 applications. Stored processes are easy enough to use out of the box but knowing how to customize their appearance and behavior requires a broader understanding of how they operate. This paper will explore how to customize stored processes with SAS 9.2 and take control of their inputs and outputs. The ability to parse information directly via a URL, customize the prompts user interface and control their operation within desktop and web applications will be discussed. This paper will be useful for beginner through to advanced SAS users who have some prior experience of using the SAS 9 applications | macro variable, sample, sas enterprise guide, file statement | source | 16 |
| 321 | Unveiling the Power of Cascading Style Sheets (CSS) in ODS | Kevin D. Smith | Prior to SAS 9.2, PROC TEMPLATE was the only choice for writing ODS styles. SAS 9.2 added the capability of writing styles using Cascading Style Sheets (CSS) syntax . However, the accepted CSS syntax was primarily a remapping of PROC TEMPLATE elements and attributes to CSS classes and properties. While this remapped syntax was a step in the right direction, no new capabilities were added to the styles themselves. CSS support in SAS 9.3 takes a bigger leap toward the power of CSS seen on the Internet today . While most people think of CSS as an HTML-only technology, it is a style syntax that can also be applied to other ODS outputs such as PDF, RTF, and ExcelXP. In fact, CSS in SAS 9.3 work s with any of the ODS outputs that use styles. This paper uncover s the hidden power of CSS in SAS 9.3 and show s you things that were never before possible in previous versions of SAS as well as directions SAS will be taking in the future |
proc template, proc step, ods html, dataset | source | 23 |
| 322 | Up To Speed With Categorical Data Analysis | Gary G. Koch, Maura E. Stokes |
Categorical data analysis remains a substantial tool in the practice of statistics, and its techniques continue to evolve with time. This paper reviews some of the basic tenets of categorical data analysis today and describes the newer techniques that have become common practice. The use of exact methods has expanded, including additional assess- ments of statistical hypotheses, conditional logistic regression, and Poisson regression. Bayesian methods are now available for logistic regression and Poisson regression, and graphics are a regular component of many analyses. This paper describes recent techniques and illustrates them with examples that use SAS/STAT software |
confidence interval, regression analysis, logistic regression, logistic distribution, sample, proc genmod, poisson regression, likelihood function, predictor variable, binomial distribution, poisson distribution, proc step, bayesian statistics, conditional logistic regression, maximum likelihood estimation, null hypothesis, proc freq, estimate, sample size, clinical trial, exact test, proc logistic, covariance, posterior probability, randomization | source | 25 |
| 323 | Use Customer Analytics in Your Processes. Yes, But How? | Pushkraj V. Marathe | As a retailer, you always aspire to offer the products and promotions that your customers desire. The challenge: Gain the insights portraying your customers' real "desires", and then imbibe those correctly in your retail processes. The current paper highlights the need and ways to utilize customer analytics. It focuses on challenges and tries to explore workarounds. It further explores the "need" to use customer analytics across varying degree of retailer readiness and formats. It offers a sample case study using retail industry solutions | market basket analysis, subscriber analytics, proc freq, association rules | source | 11 |
| 324 | User-Friendly Childhood Obesity Screening Charts Using SAS/STAT Graphics | George C. J. Fernandez | Epidemiological data reveal that 31.9% of U.S. schoolchildren are overweight or obese. The 85th and 95th percentile values of body mass index (BMI) are the accepted screening measures for overweight and obesity respectively for children two years of age and older. While having epidemiological and diagnostic values, the BMI is a measurement not readily available to compute and assess for the layperson. The Center for Disease Control (CDC) 's gender- and age-specific percentile BMI EXCEL data tables for the U.S. population were used as the source to compute the underweight, normal, overweight, and obese weight ranges (in pounds) by converting percentile BMI values to weight limits by (BMIHTHT/703 ) where HT is the child's height in inches |
excel, advanced sas | source | 8 |
| 325 | Using a JMP Add-In with SAS to Evaluate the Measurement Process: Going Beyond the Traditional Gage R&R | Jose G. Ramirez, Jon T. Weisz |
In this paper we demonstrate how the JMP Scripting Language (JSL) was used to create an application to convert a SAS/INTRNET web app, designed for performing EMP (Evaluating the Measurement Process) studies, to run from within JMP. The original SAS code was not modified, but the html form code was converted to JSL and some of the SAS graphical output was rewritten to use native interactive JMP graphics. The JMP app takes advantage of the many SAS analytical procedures (PROC SHEWHART, PROC MIXED, PROC ANOM), and the powerful visualization tools that the JMP environment provides. This gives the user an easy and efficient way of analyzing EMP studies. |
response variable, sas output | source | 11 |
| 326 | Using CALL SYMPUT to Simplify | W. Jodi Auyuen | Automation is a commonly used word these days for companies who want to streamline their processes and for SAS programmers who want to make significant improvements in their programs to increase efficiency, and to make work and life simple. The SAS system provides us with powerful and handy tools for automation. It helps to avoid potential human errors, to cut down editing time, to guarantee accuracy and consistency, to provide fast turnaround time, to require less human intervention, and last but not least, it offers great opportunities to gain knowledge and experience to share with colleagues. This paper demonstrates one example using CALL SYMPUT, INTNX, and SAS macros to reduce manual steps so as to consolidate five to seven SAS programs into one SAS program. It simplifies the work process and life! | macro variable, call symput, macro definition, macro put, put statement | source | 7 |
| 327 | Using Hash Tables to Obtain Matched Post-Hoc Control Populations | Jeffrey Reiss, Elayne Reiss |
In social science and other settings, analysts are often asked to conduct post-hoc studies to measure the effectiveness of a particular program or treatment. In other words, those implementing the program have not identified an appropriate control group for comparison purposes, although it may be crucially necessary in order to conduct an effective analysis of the study. For example, in some educational studies, an intervention may target a very specific type of student with a particular academic background and demographic profile, so the creation of a closely matched control group-created after the fact-is essential to quality data comparison. This paper explores a straightforward yet handy use of the hash table, a data structure available in Base SAS, to turn a large database of mostly "mismatched" potential control group observations into a much more comparable sampling population | hash table, interval variable, sample, proc sort, index, key, memory usage, proc step | source | 7 |
| 328 | Using JMP Visualizations to Build a Statistical Model | George J. Hurley | JMP has long been used by engineers to build various types of models, including statistical models. However, many statisticians and business analysts have chosen to remain within the SAS/STAT package to develop their statistical models, possibly due to the historical relationship between statisticians, the pharmaceutical industry, and FDA submissions. However, statisticians are becoming much more common in the business analytics fields, including marketing and pricing. Given that these areas do not have the stringent coding guidelines of FDA oversight, it is natural that statisticians should evolve and begin using some of the more interactive and insightful tools available in conjunction with traditional SAS/STAT. JMP is the perfect tool to add to the portfolio. This is because the visualization elements of JMP and simple interface make developing statistical models highly intuitive with JMP |
neural network, proc reg, dataset, interaction effect, proc step, sample, regression analysis, data cleaning | source | 11 |
| 329 | Using Microsoft Access 2007 Description of Field Names in Tables as Variable Labels in SAS Data Sets | Maria S. Melguizo, Jerry Stalnaker, Christopher J. Swearingen |
It is a common practice for statisticians and data analysts to import data from Microsoft Access into SAS for analysis. Although it is relatively straightforward to transfer data between the two programs, currently there is not an option in SAS to import the description of field names from Access tables. Any good SAS programmer would be interested in using those descriptions as labels for variables in SAS data sets. This paper proposes a solution using Visual Basic for Applications (VBA) and SAS macros, first to generate a table of field names, descriptions, and table names in Access; and second, to use this table to create the labels in the SAS data sets | dataset, data science | source | 5 |
| 330 | Using Multi-Label Formats to Create Subtotals in PROC TABULATE | Christina Morris | PROC TABULATE is a procedure in SAS that is used to create a table of statistical data. Multi-label formats can easily help create otherwise difficult subtotals in a PROC TABULATE table. In the PROC FORMAT procedure, you can create format values based on multiple values of the variable being formatted. When you define a multi-label format, you can then use the format with PROC TABULATE to provide subtotals for your data. This paper will illustrate situations where a subtotal is needed in a report. It will demonstrate how to use multi-label formats with PROC FORMAT and PROC TABULATE to create tables with subtotals in Base SAS 9.2 in a Z/OS environment |
proc step, proc tabulate, proc format, format statement, char. | source | 4 |
| 331 | Using ODS TAGSETS.HTMLPANELS to Create Quick Dashboards | Gabe Cano | I like GREPLAY and ANNOTATE… I really do. But there are times when you need to quickly share a draft of your output with a colleague or a client. One quick and fast way to do this is to write your listing or graphics output to an ODS document such as RTF, PDF, HTML etc. But what if you are creating a complex report such as a dashboard? This gets a little more complicated since graphs and tables need to be placed in specific locations. This presentation will show ways to send your dashboard to an ODS conduit called TAGSETS.HTMLPANEL. This ODS option allows you to quickly create compartmentalized output without having to define or manage the details of your dashboard |
proc step, proc print, ods html, output delivery system | source | 13 |
| 332 | Using PROC LOGISTIC to Estimate the Rasch Model | Tianshu Pan, Yumin Chen |
This paper describes how to use PROC LOGISTIC to estimate the Rasch model and make its estimates consistent with the results of the standard Rasch model software WINSTEPS |
proc logistic, logistic distribution, proc step, estimate, logistic regression, maximum likelihood estimation, regression analysis, likelihood function, binary variable, proc genmod | source | 7 |
| 333 | Using Real-Time Analytics to Manage Next Best Offer | Christopher R. Toth | How can you continually increase the value of customer interactions? Typical campaign management and batch processes look only backward. They do not account for the real-time context of the customer interaction. For example-the reason for a call or visit to your Web site, the demeanor of the customer, the product interest that the customer has-these factors and characteristics are dynamic and evolving. This paper discusses how SAS Real- Time Decision Manager helps you make your offers relevant to the interaction and behavior, which, in turn, increases customer satisfaction and improves offer response rates | next best action, dropped call, customer satisfaction, predictive analytics, data mining, credit score | source | 5 |
| 334 | Using SAS Enterprise Guide to Provide Water and Conserve Electricity for SAS Global Forum Attendees! | Cecelia A. Adair | Did you enjoy your shower this morning? Say "thank you" to SAS Enterprise Guide for making our utility data accessible and useful. This presentation describes the progress from "What's SAS?" to "Significant Contribution!" that has been accomplished at the Las Vegas Valley Water District for our utility operations and long-term rehabilitation planning. Join me for a look at real world utility data and software applications. Learn how Las Vegas Valley Water District was able to leverage information from across the company to find opportunities to save water, reduce the guesswork of rehabilitation, reduce failures, get important information faster and more accurately, and improve data quality. In the process, you also learn a bit about water delivery and the important factors of this capital-intensive business, which may help you understand your own water department issues at home. You can change the course of the future with SAS Enterprise Guide | sas enterprise guide, data cleaning, sample | source | 10 |
| 335 | Using SAS Enterprise Miner for Resort Profit Optimization: How to Systematically Provide Individual Consumers with Personalized Rates and Offers to Unleash Total Profit Optimization | Marco Benvenuti | The continued increase in competition in the gaming and resort industry combined with one of the worst recessions in history make traditional revenue management and marketing strategies ineffective and obsolete. Traditional approaches maximize t op-line revenue or, at best, they maximize the income of a specific segment of business. Unfortunately, most companies are using traditional methods and during the recession have focused on deep discounts and cost-cutting because there did not seem to be a ny other options. Profit optimization is the ideal solution because by providing individual consumers with personalized rates and offers resorts can offer high value services and products to customers willing to pay for them while discounting selectively t o price-sensitive customers | data mining, revenue management, data warehouse | source | 6 |
| 336 | Using SAS for Effective Credit Risk Management of the Commercial and Corporate Credit Portfolio after the Financial Crisis | Boaz Galinson | This paper addresses two main lessons: The first one is that credit risk management should be much more than just an add on to the credit process, but effectively implemented within the credit process and throughout decisions making. The second one is how to create a complete view and understanding of the risk profile of the credit portfolio. A showcase of Effective Credit Risk Management with SAS describes the different stages of such of a project: the credit risk data base, the modeling of the risk parameters and the necessary IT modules to support advanced credit risk methodology | credit risk, credit portfolio, concentration risk, economic capital | source | 10 |
| 337 | Using SAS Formats: So Much More than "M" = "Male" | Peter J. Lund | Formats in SAS can be used to change the way that a value is displayed. There are numerous formats supplied by SAS for controlling the way dates, times, numbers, currencies and other types of values appear. You can also use PROC FORMAT to build your own formats and create labels for your data values. This paper will look at a number of features of PROC FORMAT: using value ranges, having open -ended ranges (with the LOW and HIGH range values), capturing left-overs (with OTHER), nesting one format inside another, controlling the sort order of the formatted values (with the NOTSORTED option), and allowing a value to fall into more than one range (with the MULTILABEL option). Formats allow much more than just changing the labels that are displayed for your data. SAS procedur es analyze data at the formatted value level, which means that you can do different levels of analysis just by changing the format, without having to create new variables |
proc step, proc format, format statement, put statement, proc tabulate, sysdate9, character variable, proc freq, date variable, missing value, char. | source | 14 |
| 338 | Using SAS on UNIX with Multiple Active Directories as Authentication Providers | Jan Bigalke | Using SAS on Unix / Linux servers with active directories as authentication providers is now a common approach. This paper explains the basics of Unix authentication against active directories. Limitations of using standard PAM modules for authenticating users stored in multiple domains are explained. Various solutions are required to address challenges for the different access methods used by clients to connect to SAS, e.g. web access and client applications like Enterprise Guide | sas enterprise guide, api | source | 6 |
| 339 | Using SAS PROC TCALIS for Multigroup Structural Equation Modeling with Mean Structures | Fei Gu, Wei Wu |
Multigroup structural equation modeling (SEM) is a frequently used technique to evaluate measurement invariance in social and behavioral science research. Before the 9.2 version, SAS was incapable of handling multigroup SEM, but this limitation is resolved in PROC TCALIS in SAS 9.2. For the purpose of illustration, this article provides step-by- step guide to programming the tests of measurement invariance and partial invariance using PROC TCALIS for multigroup SEM with mean structures. Fit indices and parameter estimates are validated, thus providing an alternative tool for researchers who conduct both applied and simulated studies. Other new features (e.g., different types of modeling languages and estimation methods) and limitations (e.g., ordered-categorical SEM and multilevel SEM) of the TCALIS procedure are also briefly mentioned |
proc step, covariance, factor loading, sample, sample size, proc calis, degrees of freedom, covariance structure, estimate | source | 16 |
| 340 | Using SAS Procedures FREQ, GENMOD, LOGISTIC, and PHREG to Estimate Adjusted Relative Risks: A Case Study | Jiming Fang | We present nine methods to compute an adjusted relative risk (RR). These methods evolved over the past 25 years (1985-2010) via SAS/STAT procedures: FREQ, GENMOD, LOGISTIC, and PHREG. We also compare the strengths and limitations of these methods, using an observational cohort study for illustration |
regression analysis, poisson regression, poisson distribution, binomial distribution, logistic distribution, sample, logistic regression, proc step, bootstrap, propensity score, proc genmod, proc logistic, odds ratio, relative risk, sample size, proc freq, proc phreg | source | 11 |
| 341 | Using SAS Software for Iterative Data Integration (DI) | Evangeline Collado | This large, metropolitan university wanted to know if its graduate students are returning each semester and continuing to completion of their degree program (i.e., retention and graduation statistics). To accomplish this analysis a new data mart that contained all of the historical graduate cohorts, beginning with the 1997-1998 cohort year, and their enrollment information was required. A robust data integration (DI) process had to be developed that would loop through each cohort year, collect the data from multiple tables in the operational system for each master's, doctoral, and specialist student and load it into the data warehouse. After the cohorts were created, another DI process was needed to update the data mart with enrollment statistics for each subsequent year. We were able to accomplish this huge iterative task using a combination of SAS Data Integration Studio, SAS Stored Processes, SAS macro variable, and SAS Enterprise Guide | macro variable, sas enterprise guide, data integration, data warehouse, excel, proc sql, macro definition | source | 10 |
| 342 | Using SAS Stored Processes with JavaScript and Flash to Build Web-Based Applications | Philip A. Mason | This paper describes techniques and ideas I have used for over five years as I built complex applications using the SAS Stored Process Web application. The Web application enables the output from stored processes to be delivered in various ways (including HTML) by using the SAS Output Delivery System. You can enhance the output by using JavaScript to add a lot of extra functionality, particularly interactivity. An easy way to add JavaScript functionality is to use some JavaScript programs written by exper ts. These programs can be easily found on the Web and are great for adding a feature or function here and there. However, a more powerful and structured way to add functionality is to make use of a JavaScript application framework. These frameworks are use d by most of the best- known Web sites in the world. This paper describes how to use the Ext JS framework, which is one of the best JavaScript application frameworks | macro variable, %if-%then-%else, sample, macro definition, dataset, sas enterprise guide, sas 9, excel, where statement, proc step | source | 27 |
| 343 | Using SAS to Automatically Back Up Files across Multiple Directories | Laura Lawrence | Often times we need to reference earlier work done on a project. Therefore it is necessary to save many different versions of certain files as we work. For example, you might need to compare a report you did 2 months ago to a more recent report or you need to compare an older SAS data set to a newer one. Therefore you must have all these older versions saved in a location that is easy to identify. This paper will take you through a macro useful in backing up various files of any type. The purpose being to create a new folder, named as the last modified date of the files to be copied, and copy ove r all files of the user -specified directory. This macro is set up so it can be run over multiple directories with ease. It works on the assumption that you want the entire directory backed up. This would come in handy when the programmer is responsible for saving all versions of their code and other files, such as the SAS data sets | macro variable, dataset | source | 4 |
| 344 | Using SAS to Create Custom Graphics | Barbara B. Okerson | SAS graphics products can be used to create and deliver high-impact visuals, both as presentation-ready graphics and as data exploration displays. Using these graphics, users can explore, examine, and present data in an understandable manner while distributing their findings in a variety of formats to decision makers who can gain a quick, visual understanding of critical issues. Although SAS provides many ready-to-use graphics and graphic formats through SAS/GRAPH software and Output Delivery System (ODS) graphics, sometimes other graphics displays are needed. SAS provides the tools to create almost any desired graphic. This paper shows examples of how SAS can be used to create graphics that are not directly available. Results included in this paper were created with SAS 9.1.3 or SAS 9.2 on a Windows XP platform, using Base SAS, SAS/STAT software, and SAS/GRAPH. SAS 9.1 or later is required for ODS graphics extensions |
waterfall chart, proc sgplot, proc step, proc gplot | source | 10 |
| 345 | Using SAS to Enter Variables in a DOS Window | Brandon Harvey, Robert D. Parker |
Do you have an old DOS application that prompts you to type responses in the DOS window? Let SAS do it for you. This paper covers all the code that you need to replace the manual process of key-entering responses to DOS window prompts. You can automate the entire process. Simply use SAS to build a text file of the responses you would have otherwise had to type into the DOS window manually and tell DOS to use that file. Add a few data driven macro techniques and you have a complete automated solution. While the solution presented below is specific to a particular DOS program (GF98), it may be applied to any DOS program that requires responses to prompts | txt, macro variable, macro definition | source | 7 |
| 346 | Using SAS to Help Fight Crime: Scraping and Reporting Inmate Data | William G. Roehl | A colleague posed an interesting question recently regarding the makeup of inmates incarcerated within the Dakota County, Minnesota jail system. While limited summary data is made available by the county every year in clunky Microsoft Word tables, the information provided would not help to answer the question. Fortunately the county also provides a web interface to view inmates currently incarcerated in the Dakota County Jail. With informative data including ethnicity, gender, age, arrest date and city being made available to the public, what better way to collect and report on data to answer this question but by pulling directly from the source and not having to rely solely on infrequently updated summary tables? The only problem is that the very rich data on the Dakota County Inmate Search website is not in a format conducive to reporting and thus some massaging must occur to make it useful for this purpose | flat file, raw data | source | 7 |
| 347 | Using SAS to Identify and Stratify Members of Disease Management Programs at Community Care Behavioral Health Organization | Michele Mesiano, Meghna Parthasarathy |
Community Care Behavioral Health Organization (Community Care) created a Disease Management program in July of 2009 to support their members in their behavioral health recovery efforts. Community Care chose to have their Disease Management program accredit ed by the National Committee for Quality Assurance (NCQA). The guidelines for this accreditation are very detailed and require that specific data types be used to identify and stratify program members. In order to meet NCQA requirements, Community Care had to write several programs that would pull data from disparate data sources located on different technical platforms and merge that data for further processing and reporting. This type of data gathering and analysis would typically require manual transform ation of data. However, the power of SAS enabled Community Care to automate this process. Data from two different platforms were linked using the local machine as an intermediary | population, libname statement, proc sql, sql server, dataset | source | 8 |
| 348 | Using SAS to Incorporate the SDTM for a Study Funded by the NIH | Fenghsiu Su, Bradford Jackson, Kaming Lo, Sumihiro Suzuki, Karan Singh, David Coultas |
Since early 2000, the electronic submission for drug approval has become more common. Many pharmaceutical companies that were previously without standardized electronic databases started to adopt the Clinical Data Interchange Standards Consortium (CDISC) standard. The Case Report Form (CRF) data are converted as study data tabulation models (SDTM). The US Food and Drug Administration (FDA) and CDISC have begun to work together to standardize the format to improve the review and approval process of New Drug Applications (NDAs). The purpose of CDISC is not only to improve efficiencies in the pharmaceutical industry but all health-related fields. The challenges to implement CDISC standards for National Institutes Health (NIH) based research are that people are not aware of what CDISC is and how to incorporate it into their study. This paper demonstrates how CDISC can be easily applied in an NIH-funded study by using SAS and comments on the benefits of using it | crf, sdtm domain, data cleaning, cdisc | source | 8 |
| 349 | Using SAS to Model and Forecast Greenhouse Gas Abatement Cost Curves | Lorry Hardt Jr., Keith Renison |
In the McKinsey paper titled "Pathways to a low-Carbon economy," the authors provide "…a quantitative basis for... what actions would be most effective in delivering emissions reductions, and what they might cost." Many such actions, known as abatement projects, result in a net negative cost, and as such present serious opportunity to those who can determine which ones to do, when, and to what extent. However, when practitioners begin to dig deeper, they find the modeling challenge and tangential constraints too complex for ordinary spreadsheet tools they have been used to using. This paper discusses the challenges surrounding greenhouse gas abatement cost curves and provides examples of how a combination of SAS portfolio optimization modeling and energy and emissions forecasting techniques can help make the right decisions, and improve both environmental and economic performance | proc step, odds ratio | source | 10 |
| 350 | Using SAS to Move Data between Servers | John E. Bentley | In today's complex data management environments, it is not unusual for UNIX servers to be dedicated to a particular department, purpose, or database. As a result, a SAS data analyst often works with multiple servers, each with its own data storage environment. For security reasons, our IT partners often do not make it easy for the servers to talk to one another. So how do we transfer data between servers? SAS provides a number of solutions. This paper shows ways to use Remote Compute Services and SAS Data Transfer Services along with Procs and Data steps to move data between servers. Code samples are provided. This presentation is designed for all levels of SAS programmers and is relevant to SAS Enterprise Guide users as well | macro variable, proc step, traffic data, ftp, sas base, cpu time, work library, dataset, data cleaning | source | 12 |
| 351 | Using SAS/GRAPH Annotate to Create Customized Control Charts | Sailesh Vezzu | The Annotate facility in SAS/GRAPH is a very powerful tool to generate customized plots. This paper is a direct result of one of my projects at work, where I had more than one observation at the same point in the plot. In this paper, I introduce a creative way to represent multiple numbers of observations at various points in the plot and show how to implement this using SAS Annotate. I also show how to use SAS Annotate to add custom notes for some points in the plot and to make some outlying observations stand out in the plot by using a different color or symbol |
proc step, proc gplot | source | 5 |
| 352 | Using the PRELOADFMT Option with User-Defined Formats to Create Summary Tables | Suwen Li, Yi Hua, Daniel Li, Bob Lan |
Occasionally people need to summarize data with all possible combinations of the class-level values to appear in the summary table, even if the data did not contain some of the class-level values, or display the output according to a specified order. Although we can accomplish this task with powerful SAS DATA steps, the PRELOADFMT option with user-defined formats can achieve this easily without much programming work | proc step, proc report, user-defined format, class variable, proc format, proc means, adverse events, proc tabulate, missing value, clinical trial | source | 11 |
| 353 | Using the SAS Macro Language and SAS Pipe to Process State Health Care Survey Data | Xiaoting Qin, Cathy Bailey |
States collect survey data continuously through the year using a standardized questionnaire from the Centers for Disease Control and Prevention (CDC). CDC receives data files from 40 states on an unscheduled basis. Monitoring each state's data collection by importing multiple ASCII files, converting ASCII files into SAS data sets, and compiling the SAS data sets for processing can be extremely time consuming and challenging for a SAS programmer. In this paper, we discuss a solution to automatically read 1-12 ASCII files from 40 state's folders on a network drive, convert multiple ASCII files to SAS data sets, and merge all of the SAS data sets into one aggregate SAS data set. We used Base SAS, the SAS macro language, and the Microsoft Windows operating system for the entire process. This method can be applied to other state-based survey data processing to efficiently control survey data quality | dataset, macro facility, macro definition, data wrangling, sample | source | 6 |
| 354 | Visualize Your Cloud Data Using the Graph Template Language | Krishnan Raghupathi | As the adoption of cloud computing by enterprise companies picks up, there will be an exponential growth in the volume of data that gets stored on the cloud. Under such a scenario, there will always be a need to analyze your cloud data in a seamless manner. This paper demonstrates how one can write a SAS program, using a combination of PROC SOAP and PROC TEMPLATE, that can provide an effective visual representation of data that is available in the cloud. Reporting and Information VisualizationSAS Global Forum 2011 Visualize Your Cloud Data Using Graph Template Language, continued 2 |
proc step, amazon s3, dataset, proc template, web service, data science | source | 12 |
| 355 | Visualizing Count Data with 3D Flash Animation in a SAS Environment | Xin Wei | Modern data visualization technology requires the integration of multiple tools and platforms. This paper describes using Base SAS to wrap a JavaScript, a Flash object, and XML in HTML text in order to visualize the word count of the pharmaceutical SAS user conference proceedings from 2000 to 2010 in three-dimensional animation | proc freq, sas base, macro variable | source | 7 |
| 356 | Visualizing Marginal Effects from Interactions in Generalized Linear Models | Xiao Chen, Brigid Wilson |
Interactions are often included in generalized linear models (GLM). Interpreting these interactions in the transformed scale of the linear equation is like interpreting an interaction in an OLS regression. However, this is rarely the scale in which results are discussed, and interpreting interactions in the non-transformed scale is not straightforward. Focusing on a continuous by categorical interaction in a logistic regression, we present code for visualizing a marginal effect in the probability scale. This visualization is a useful tool for understanding a model effect that is difficult to intuit from the standard model output. By presenting the results in the researcher's scale of interest, it allows the researcher to better communicate the model results | logistic distribution, confidence interval, proc genmod, proc step, interaction effect, regression analysis, proc logistic, sample, nominal variable, logistic regression, inferential statistics, bootstrap, proc plm | source | 7 |
| 357 | What Are People Saying about Your Company, Your Products, or Your Brand? | Kathy Lange, Saratendu Sethi |
A growing area of interest for many firms is understanding what the market (customers, analysts, or key opinion leaders) is saying about their products and services. Many refer to this area of analysis as sentiment analysis. They want to understand more about people's opinions, attitudes, and emotions when discussing their products, services, or overall brand. From the company perspective, listening and analyzing what people are saying about products and services is the first step in creating a dialogue with that audience by listening, learning, and then engaging with them. This dialogue can better inform targeted marketing initiatives to customers and prospects, enabling the organization to communicate at a significantly lower cost than traditional marketing with increased speed and effectiveness. It can also enable a more rapid response to perceived customer issues and competitive threats | proc hpsplit, customer experience | source | 16 |
| 358 | What CDISC Means to SAS Programmers | Kevin Lee | The paper is intended for SAS programmers in the pharmaceutical industry who are interested in SAS programming in a CDISC environment. This paper discusses the basic structure of CDISC (Clinical Data Interchange Standards Consortium) and the current roles of SAS programmers in a CDISC environment. It also discusses the future of SAS programmers in a CDISC environment | sas programmer, clinical trial, proc step, sas programming, clinical data, proc phreg, cdisc | source | 3 |
| 359 | What Happens When You Push the Easy Button? Migrating to SAS 9.2 | Faron Kincheloe | It has been said that the utility for migrating a Business Intelligence (BI) installation from version 9.1 to version 9.2 is supposed to be like pushing the "Easy Button ". Baylor University has successfully completed a migration of Enterprise Business Intelligence to version 9.2. This paper will describe the migration experience and present some lessons learned in the process | credit migration, business intelligence | source | 8 |
| 360 | What Were We Talking About at Those SAS Conferences, or Let's Make Some Tag Clouds | Chang Y. Chung, John Henry King |
A tag cloud is a bunch of tags or words displayed nice ly. More popular or important words are highlighted using different colors or font sizes. Popula rized by Web 2.0 web sites, tag clouds can be informative and useful. This paper demonstrates making tag clouds using nothing but Base SAS. The keywords come from the titles of all papers presented at two international SAS conferences: the 25th SAS Users Group International Conference in 2000 (SUGI 25) and the SAS Global Forum 2010 | proc step, proc freq, lookup, data step, proc sql, sas base, conditional do loop, input statement | source | 11 |
| 361 | What's Hot, What's Not - Skills for SAS Professionals | Kirk Paul Lafler, Charles E. Shipp |
As a new generation of SA S user emerges, current and prior generations of users have an extensive array of procedures, programming tools, approaches and techniques to choose from. This presentation identifies and explores the areas that are hot and not -so-hot in the world of the professional SAS user. Topics include Enterprise Guide, PROC SQL, PROC REPORT, PROC FORMAT, Macro Language, ODS, DATA STEP programming techniques such as arrays and hashing, business intelligence, sasCommunity.org, JMP, SAS/GRAPH, SAS/STAT, and |
sas 9, sas base, proc step | source | 6 |
| 362 | What's New in SAS Data Integration | Nancy A. Rausch, Michael Ames, David Barkaway, Jeff Stander |
SAS Data Integration Studio 4.3 provides many new enhancements to help both data warehouse developers and data integration specialists carry out data-oriented processes more efficiently and with greater control and flexibility. A major focus of the release is to deliver new features to help bring code into the managed environment using the SAS Code Analyzer, provide performance optimizations through DBMS pushdown, and support Enterprise-level development with integrated versioning and rollback support. The introduction of the complimentary SAS Data Management product helps leverage the capabilities provided in SAS Data Integration Studio and introduces new capabilities around data visualization, job management, parallelization, and database integration. Customers will find many reasons to upgrade to the latest version of SAS Data Integration Studio | proc sql, data visualization, data integration, scd type 1 | source | 13 |
| 363 | When ""Anydate"" Doesn't Mean Any Date: A Macro Solution | Charlotte King, John Fleming |
In our work, we often receive administrative and research data in Microsoft EXCEL workbooks. This data is used for developing standardized approaches to administrative reporting and for research projects. A lot of it contains dates that don't conform to either EXCEL or SAS date formats, and are rarely entered consistently in the EXCEL workbook. In the past, a secretary would spend hours going through these workbooks reformatting dates so they were in a form that SAS could understand. In SAS9, SAS introduced the ANYDATE INFORMAT that can read data in a variety of date formats. This has its limitations, too. To accommodate these limitations, we developed a SAS macro. With our SAS macro, we have converted reading dirty date data from non-SAS sources from a labor-intensive process into an easy routine task, saving hours of time and reducing data entry errors resulting from the manual re-entry of dates |
excel, date variable, xls, sysdate9, format statement, macro definition, sample | source | 7 |
| 364 | While You Were Sleeping, SAS Was Hard At Work | Andrea Wainwright-Zimmerman | Automating and scheduling SAS code to run over night has m any advantages, but there are also many pitfalls to be aware of. This paper discusses how to make sure you save t he log, get e-mail messages at critical points, as well as makes other recommendations and considerations on how to make it all work | libname statement, exception handling, proc freq, macro definition | source | 9 |
| 365 | Who Do You Have? Where Are They? | William C. Murphy | Questions like this are often asked when dealing with a database consisting of more than one SAS data set: What subjects do you have? In what data sets do they provide data? Is the data in the different data sets consistent? If you had only 2 or 3 small data sets, you might just open them up with the SAS viewer and resolve these questions by inspection. However, when your database consists of dozens, if not hundreds, of data sets, your approach must be different. In our analyses, we use the SAS DICTIONARY tables to obtain lists of the relevant data sets in our database. Using this information, along with a macro DO-loop, we are able to create cross tabulations of the database. These tabulations not only provide lists of the subjects that are present in the different data sets but also can be used to find inconsistencies among the common variables. A simple macro program that produces these tabulations will be presented | dictionary tables, macro variable, macro definition | source | 6 |
| 366 | Why .1 + .1 Might Not Equal .2 and Other Pitfalls of Floating-Point Arithmetic | Clarke Thacher | All numeric values in SAS are represented as 64-bit floating point numbers. Floating-point representation can store a wide range of values. It is very important for every programmer using SAS to understand how floating point arithmetic works and to be aware of the limits of floating point arithmetic. In this paper, we give a detailed explanation of floating point representation and demonstrate some of the common mistakes that can be made by SAS programmers. We also offer suggestions for avoiding these traps. We show: Why .1 + .1 may not equal .2 How the rules of algebra may not apply to floating point numbers Why it isn't a good idea to store a twenty digit identifier in a floating point number. How sorting may affect numeric results We demonstrate these principles through a series of easy-to-understand SAS programs. This paper requires no previous knowledge, but it should be valuable to every SAS programmer | decimal, missing value, numeric variable, sas programmer, note | source | 19 |
| 367 | Why Did the Le Mans Risk Platform at Lloyds Banking Group Win the 2010 SAS Innovation Award? Hear the Story So Far. | Ruth Heaven | Lloyds Banking Group, the largest high street bank in the UK, has recently developed and implemented a new Risk Platform (Le Mans) to support the Credit Risk community. The Le Mans infrastructure utilises the suite of SAS 9.2 banking solutions and its stated aim is to enable 21st Century Analytics. The presentation will cover the business case presented, rationale for our selection of SAS, solution and approach as well as the challenges faced in delivering a step change within the business through adoption of new technologies and overcoming challenges to the vision | sas grid, sas enterprise guide, sas data integration studio | source | 9 |
| 368 | Why Government Can Take the Lead in Fighting Fraud, and Use the SAS Fraud Framework to Get There | Carl Hammersburg | The Washington State Department of Labor and Industries has built a state-of-the art tool for detecting and preventing workers' compensation premium fraud, based upon the SAS Fraud Framework and Social Network Analysis. This paper details the history of our path, and the broader opportunity for government institutions to take a leadership role in fighting fraud | workers compensation, social network analysis, outlier | source | 8 |
| 369 | Why Should You Be Using the New SG (Statistical Graphics) Procedures in SAS 9.2? | Philip R. Holland | There is always a hope that a new software feature will improve on what already exists, but fear of the unknown stops you from trying it out, just in case it is a waste of time. This paper tries out the new SG (Statistical Graphics) procedures in SAS 9.2 for you, and demonstrates some of the new and useful features of PROC SGPLOT, PROC SGPANEL, PROC SGSCATTER and PROC SGRENDER |
proc step, proc gplot, proc sgplot, proc sgpanel, proc sgscatter, proc template, macro variable, proc gchart, proc greplay | source | 11 |
| 370 | Windows Application Using .NET and SAS to Produce Custom Rater Reliability Reports | Sailesh Vezzu | Some of the common measures used to monitor rater reliability of constructed response items include Percent Exact Agreement, Percent Adjacent Agreement, Simple Kappa Coefficient, Weighted (Cicchetti-Allison, Fleiss-Cohen) Kappa Coefficient, Pearson Correlation Coefficient, Mean Difference, Standardized Mean Difference, and Matched-Pair T-Test. This paper discusses the development of a Windows application using Visual C#.NET and SAS to calculate various measures of rater reliability and produce custom reports. Users can select which measures to include in their reports and set threshold values to flag items with low rater reliability values | proc step, proc freq, sas log, weighted kappa, pearson correlation, standard deviation, correlation, paired t-test, macro definition, %if-%then-%else | source | 13 |
| 371 | Work Smarter Rather than Harder - Tools for Growing up A SAS Programmer | Jiangtang Hu | The way we use tools, whether we like it or not, influences our behavior styles and thinking habits. Utilizing tools is the first stage to grow up a programmer (and a SAS programmer as well). This talk presents a few simple and powerful tools to arm an efficient and productive SAS programmer: a text editor (Notepad++), a files comparing tool (Beyond Compare), and information aggregators (Gmail/Google Reader). Notepad++ is a powerful and flexible text editor (also simple and FREE) to supplement the shortcomings of the built in SAS Enhanced Editor. Using Notepad++ will significantly improve your user experiences and efficiency in your daily typing and editing work. From an ergonomic point of view, it will also save your hands and fingers. The GUI tool Beyond Compare can compare almost every type of file (including pictures, audios). It is extremely useful in both production and validation programming cycles to compare different program versions (.sas, . . .), outputs (.txt, .dat, .rtf, | sas programmer, sas enterprise guide | source | 9 |
| 372 | Working with a DBMS Using SAS Enterprise Guide | Howard Plemmons Jr., Michael Ames |
The ability to identify process issues when using a database management system (DBMS) can be difficult as you move farther away from direct SQL to the DBMS. As a SAS Enterprise Guide user, how do you ensure that you are executing optimal code for your DBMS? With many new features including in - database procedures, SQL pushdown techniques, and optimizations in SAS/ACCESS, that task can be overwhelming. This paper provides a step-by-step approach to identification and use of DBMS interaction techniques designed to help you improve your SAS Enterprise Guide experience |
sas enterprise guide, proc sql, proc step, libname statement, sas log | source | 12 |
| 373 | Writing SAS Programs with SAS and Microsoft Excel: Generate Batch SAS Programs Based on SAP Specifications | Laiju Zhang | This paper presents SASWriter the macro system for writing SAS programs that produce tables, listings, and figures for standard FDA submission. It explains the structure of the macro system by identifying the functionality of each part. It also discusses the consequences the SAS macro has for SAS programming in general in terms to programmers' relation to other team members. Key words: macro, SAP, programmers, code generation | excel, sas programmer, interval variable, proc step, data science | source | 7 |
| 374 | You Did That Report in SAS!? - The Power of the ODS.pdf Destination | Peter J. Lund | The Output Delivery System (ODS) has been around since SAS version 7 and yet many people still don't realize that they use it every day just to send results to the output window. They're still more amazed when they see that publication quality reports in PDF files can be created with SAS and ODS. This paper explores a number of ODS options in general and, more specifically, their use in creating PDF output. We will cover ODS ESCAPECHAR, which allows for inline formatting of titles, footnotes and other text and new syntax for version 9.2; ODS LAYOUT, which lets you place output wherever you want it on the page - even output from more than one procedure, both text-based output and graphics; inline formatting in PROC REPORT; the new world of DATA _NULL_ reporting using the ODS object and more. We'll work from real life examples and see how you can produce output that looks like it took hours to create |
proc report, proc step, ods pdf, put statement, data step, footnote statement, output delivery system, sas output, proc sql, histogram | source | 28 |
| 375 | You've Got ERROR | Ronald Palanca | This paper presents a convenient tool to help SAS programmers debug batch submitted SAS programs. After the execution of the program, a pop-up screen alerts the SAS programmer to any errors, warnings, uninitialized variables, or unwanted messages in the log. This program automatically gives SAS programmers a heads up about unwanted messages and specifically identifies the problems | sas log, txt, sas programmer, macro facility | source | 6 |
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