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BUGSSGF 2012 Papers - Index
365 papers. Source: SAS Global Forum conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | "There's an App for That": It's Called SAS ODS! Mobile Data Entry and Reporting via SAS ODS | Michael Drutar | Most mobile reporting allows users only to receive reports; however, it is often necessary to INPUT data on a mobile device. This can be accomplished by leveraging Google Docs spreadsheets and SAS ODS. This paper's example explains how a cross country coach records a runner's race times (in real time) on a Google spreadsheet using his mobile device. His office computer has a SAS job which, via the FILENAME statement, reads the updated Google spreadsheet into SAS. SAS processes this data and sends an e-mail containing an embedded HTML dashboard of Runner KPI dials, charts and/or tables. The coach receives this report almost instantaneously. Because it is an embedded e-mail, this solution works on nearly any mobile device (for example, iPhone or Android) |
proc step, proc report, filename statement, sas programmer, proc gchart, proc import, sas output, line chart, dataset, proc print | source | 11 |
| 2 | %SPARKY: A SAS Macro for Creating Excel Sparklines | Ted Conway | Think big? No, think small! Characterized by their small size and high data density, sparklines are information graphics that present trends and variations associated with data in a simple and condensed way. This paper describes %SPARKY, a SAS macro that creates cell-sized sparklines for embedding in EXCEL worksheets. This technique might be of interest to all skill levels. It uses Base SAS, SAS/GRAPH, t he SAS Macro Facility, EXCEL, and Visual Basic for Applications (VBA) on the PC platform |
excel, macro definition, keyword parameter, %macro parameters, macro call, bar chart, sample | source | 8 |
| 3 | 10,000 Leagues of Data... Divide and Conquer OLAP Cubes: Best Practices for High Volumes of Data | Stephen M. Overton | High volumes of data can be summarized most efficiently using OLAP technology. As volumes of data approach the millions and even billions of rows of data, loading an OLAP cube can become extremely challenging due to system constraints. This paper presents a technique that uses SAS macro programming to divide and load data incrementally into an OLAP cube. Using this technique ensures the cube will build successfully but also provides users access to the cube while it is being loaded. This paper also highlights OLAP programming techniques that allow you to streamline the cube building process and minimize the impact of errors | raw data, sample | source | 6 |
| 4 | A Bootstrapped Kappa Statistic for a Multiple-Rater Multiple-Category Problem | Yubo Gao | In many research fields, kappa coefficient is a popular tool for measuring the degree of agreement between two or more raters where the raters rate the subjects on a categorical scale. SAS procedures and macros exist for calculating kappa and its confidence interval. But this is one time calculation. Sometimes there is a need to estimate the precision of this statistic through resampling technique such as bootstrap so as to get a full understanding about its confidence interval. There is no literature so far about calculating kappa confidence intervals using bootstrap resampling methodology in a multiple-rater multiple-category situation. This article just wants to fill this gap using SAS procedure PROC SURVEYSELECT and a macro |
sample, bootstrap, confidence interval, proc surveyselect, cohen's kappa, proc step, random sampling, macro definition | source | 4 |
| 5 | A Corporate SAS Community of Support | Barbara B. Okerson | Many SAS users are not aware of an abundance of resources available to them from a variety of sources. The available resources range from those internal to their own organization to SAS itself. In order for these resources to be utilized they need to be available to the users in an accessible way. This paper shows how one large company with SAS users at many locations throughout the United States has built a highly successful collaborative community for SAS support. Modeled in the style of sasCommunity.org, the online corporate SAS community includes discussion forums, surveys, interactive training, places to upload code, tips, techniques, and links to documentation and other relevant resources that help users get their jobs done | business intelligence, sas programming, sas enterprise guide, proc sql | source | 9 |
| 6 | A Different Point of View with ODS PDF in SAS 9.3 | Scott Huntley, Woody Middleton |
The ODS PDF statement in SAS 9.3 is giving you new ways to change how you view and display your output. Several enhancements in ODS PDF 9.3 are sure to be crowd pleasers. Topics in this paper will include: how to change orientation mid-file, how to drill down from your PDF file, how a stronger use of vector-based graphics will save memory and time, and much more. Be the first to find out how to change your output and get the different point of view you've been wanting |
ods pdf, proc step, proc gchart, proc template, proc print, bar chart, options statement | source | 36 |
| 7 | A Generalized Approach to Estimating Sample Sizes | Wuchen Zhao, Arthur X. Li |
Determining sample size is one critical and important procedure for designing an experiment. The sample size for most statistical models can be easily calculated by using the POWER procedure. However, the PROC POWER cannot be used for a complicated statistical model. This paper reviews a more generalized method to estimate the sample size through a simulation approach by using SAS software. The simulation approach not only applies to the simple but also to a more complex statistical design |
sample size, sample, null hypothesis, numeric functions, proc power, proc ttest, standard deviation, type i error, type ii error, sas output, interaction effect | source | 7 |
| 8 | A Guide for Connecting Java to SAS Data Sets | Ryan Snyder, Patrick Hall |
This paper is written for Java programmers who want to access SAS data sets. We provide step-by-step examples to connect Java to SAS using three SAS technologies: * the Base SAS Java Object interface (Javaobj) | sas base, sample, sas log | source | 11 |
| 9 | A Hitchhiker's Guide for Performance Assessment and Benchmarking SAS Applications | Viraj R Kumbhakarna, Anurag Katare |
Almost every IT department today, needs some kind of an IT infrastructure to support different business processes in the organization. For a typical IT organization the ensemble of hardware, software, networking facilities may constitute the IT infrastructure. IT infrastructure is setup in order to develop, test, deliver, monitor, control or support IT services. Sometimes multiple applications may be hosted on a common server platform. With a continual increase in the user base, ever increasing volume of data and perpetual increase in number of processes required to support the growing business need, there often arises a need to upgrade the IT infrastructure. The paper discusses a stepwise approach to conduct a performance assessment and a performance benchmarking exercise required to assess the current state of the IT infrastructure (constituting the hardware and software) prior to an upgrade | cpu time, sample, predictor variable, traffic data, proc step, data science | source | 17 |
| 10 | A Macro to Summarize and Generate a Report of Variables on a SAS Data Set | Greg A. Grandits, Greg Thompson |
Before starting analysis of data from a SAS data set it is useful to have documentation and summary information of each variable before you begin working with the data set. We have developed a macro that generates a report summarizing each variable on a data set, one line per variable, which includes both numeric and character variables, and formats date variable statistics with date formats. A nicely formatted report is generated that gives the user a good understanding of the data set and is a useful reference when beginning to write SAS programs using the data set | character variable, dataset, date variable, numeric variable, standard deviation | source | 4 |
| 11 | A New Age of Data Mining in the High-Performance World | David R. Duling, Wayne Thompson, Jared Dean |
Today's businesses are challenged with both analyzing huge data volumes and improving and accelerating their predictive modeling. SAS High-Performance Data Mining technology is based on the SAS High-Performance Analytics model for distributed processing alongside the database to make use of multiple processors and vast sums of memory across multiple machines. Data mining features cover the full spectrum of building a predictive model, including data selection, exploratory analysis, transformations, feature selection, dimension reduction, linear and nonlinear modeling, and model performance comparison. New SAS High-Performance Data Mining procedures ease the transition for SAS programmers. This paper discusses the options and methods available for use in High- Performance Data Mining and uses real data for performance benchmarks | data mining, feature selection, dimensionality reduction | source | 11 |
| 12 | A New Outlook into Your Business | Anand V. Chitale, Mike Barnhart |
Business users rely on e-mail for communication and collaborative decision making. Have you ever wondered if you could use your Microsoft Outlook environment beyond a simple communication tool and get added value that keeps you more informed about your business? The integration of SAS into Microsoft Outlook provides a new outlook into your business. This integration of SAS Business Intelligence and Microsoft Outlook increases informed decision making by giving business users intuitive access to reports, stored processes, and dashboards. Business users obtain contextual information from SAS Business Intelligence while performing daily tasks in Microsoft Outlook. This paper highlights the key capabilities provided by seamless integration of SAS with Microsoft Outlook and the application of these capabilities available with SAS Add-In for Microsoft Office | business intelligence, excel | source | 9 |
| 13 | A Perfect Case of Capturing Data from Related Web Pages | Jinson J. Erinjeri | The World Wide Web generates information at a very fast pace and it can become equally important to captur e the data associated with the information at that pace. This paper deals with an application that captures the data from related web pages and converts it into a SAS data set. This application converts the source code of a web page in text format to a SAS data set using Base SAS and the same is applied across related web pages using SAS macros | dataset, filename statement | source | 6 |
| 14 | A PICTURE Is Worth a Lot of PUTs | Carol A. Martell | This paper demonstrates the use of PICTURE formats to deliver SAS data to nonstandard destinations, including OpenGIS KML for Google Earth and ArcGIS | put statement, numeric variable, macro variable, dataset | source | 8 |
| 15 | A SAS Macro for Computing Point Estimates and Confidence Intervals of Effect Sizes Associated with Mediation Analysis | Merlande Petit-Bois, Thanh V. Pham, Eun Kyeng Baek, Jeffrey D. Kromrey |
Measures of effect size are recommended to communicate information on the strength of relationships. Such information supplements the reject/fail-to-reject decision obtained in statistical hypothesis testing. Because sample effect sizes are subject to sampling error, as is any sample statistic, computing confidence intervals for these statistics is a useful strategy to represent the magnitude of uncertainty about the corresponding population effect sizes. This paper provides a SAS macro that uses bootstrapping to compute confidence intervals for an effect size associated with mediation analysis models. Using SAS/IML, the macro produces point and interval estimates of an R- squared effect size that represents the proportion of variance accounted for by the mediated effect. This paper provides the macro programming language, as well as an example of the macro call and output |
effect size, confidence interval, sample, sample size, correlation, bootstrap, confidence level, mediation analysis, dataset, predictor variable, response variable, monte carlo, macro definition, regression analysis | source | 12 |
| 16 | A SAS Macro to Zip and Unzip Files in MS Windows without Additional External Data Archiving Software | Kai Koo | AISASImacroIisIdevelopedItoIcompress/decompressIfil esIorIfoldersIinIzipIformatIwithoutItheIhelpsIfromI anyIthirdk partyI zip/unzipI applications.I ItI startsI theI defaultI CompressedI (zipped)I FolderI functionI existsI inI everyI PCI runningI underIWindowsIXPIorIlaterIversionIthroughIaIshortIVBScriptIcodeIactivatedIbyISASI%sysexecImacroIstatement.IThisI approachIeliminatesItheIdependenceIofIinstallationI ofIadditionalIauxiliaryIfilesIorIexternalIzip/unzip Isoftware,IandIitI enhancesItheIportabilityIofISASIprogramsIatIdifferentImachinesIorIworkingIenvironments.I | macro definition, dataset, macro variable | source | 5 |
| 17 | A SAS Tip-of-the-Day Web Page on an Intranet | Bruce F. Gilsen | sample, dataset | source | 12 | |
| 18 | A Simple Way of Importing from a REST Web Service into SAS in Three Lines of Code | Philip James Busby | Think old programmers can't learn new tricks? In this paper, I show two neat tricks that combine into something really clev er. (1) Our first neat trick is that instead of a path to a file on disk, the FILENAME statement can also accept a URL (v ia the URL access method). (2) Second, the SAS XML LIBNAME Engine (SXLE) can read in a static XML document f rom a FILENAME statement and turn it into a table. Combining these two, y ou can hav e SAS download a dy namically generated XML document, probably produced by a REST (representation al state transfer ) Web service (which can be built in Jav a, PHP, or .NET; it doesn't matter), and y ou can do it all in three lines of code |
web service, filename statement, libname engine, macro variable | source | 5 |
| 19 | A Simple Way to Program Power Calculations | Robert Lew, Hongsheng Wu |
We propose simulating large sets of statistical power calculations and selecting from a long list of study designs those with appropriate power. Typical commercial programs fill in all but one parameter and solve for the remaining parameter. We avoid solving equations and thereby obtain power for atypical designs. First, for simplicity, we compute the power for the test comparing two proportions without assuming either approximate normality or a common variance. Then, we extend the log-rank test power calculation to account for various patterns of on-study censoring, study duration, and patient recruitment period. We generate a new approximation for the censoring adjustment. Insodoing, we set out a easy way for others to modify our program for other related power calculations | hazard function, binomial distribution, exponential distribution, sample, type i error, log-rank test, rank test, sample size, survival analysis, proc step, numeric functions, missing value, proc means, proc print, power analysis | source | 16 |
| 20 | A Simple Yet Effective Way to Perform a Variable Cross Walk Between Multiple Data Sets | Binh C. Le, Huong T. Pham |
Analyzing data from multiple complex datasets requires that analysts have knowledge of each dataset's structure. Comparing variable names, labels, and formats between more than two complex datasets can be challenging. A data dictionary and SAS procedures such as PROC COMPARE are commonly used to complete this task. However, PROC COMPARE only allows comparison of two datasets at a time. We present relatively simple procedures that data analysts can use to identify the changes in the structures of two or more than two datasets which can pinpoint areas in which existing SAS code need to be modified to account for changes in the variable names, labels, or formats |
proc step, proc contents, label statement, proc freq, proc compare, dataset, temporary dataset | source | 7 |
| 21 | A Standard SAS Program for Corroborating OpenCDISC Error Messages | John R. Gerlach, Ganesh Sankaran Thangavel |
The freeware application OpenCDISC does a thorough job of assessing the compliance of SDTM domains to the CDISC standard. However, the application generates error and warning messages that are often vague, even confusing. Thus, it is beneficial to corroborate the CDISC compliance issues using SAS. Moreover, realizing that a validation check generates similar messages across CDISC data libraries, the SAS code should be similar, as well. Thus, paper explains a comprehensive standard SAS program that contains concise and reusable code that facilitates the process of corroborating OpenCDISC (OC) reports | proc sql, libname statement, data cleaning, create table, proc freq, where statement, proc step, by group processing, missing value, study day, proc print, frequency analysis, cdisc | source | 12 |
| 22 | A Survey of Some of the Most Useful SAS Functions | Ronald P. Cody | SAS Functions provide amazing power to your DATA STEP programming. Some of these functions are essential- some of them save you writing volumes of unnecessary code. This paper covers some of the most useful SAS functions. Some of these functions may be new to you and they will change the way you program and approach common programming tasks |
character variable, missing value, length statement, substr function, input statement, lag function, compress function, sas function, put statement, sysdate9, yrdif, character functions, numeric functions, call missing, call sortn, missing function, if-then-else | source | 16 |
| 23 | A Tutorial on the SAS Macro Language | John J. Cohen | The SAS macro language is another language layering on top of regular SAS code. Used properly, it can make programming easier and more fun . While not every program is improved by using macros -adding an- other syntax to learn and additional debugging challenges - we gain using macros as code generators, saving repetitive and tedious effort, for passing parameters to avoid hard coding, and to pa ss code fragments, ther e- by making certain tasks easier than using regular SAS alone. Macros facilitate co nditional execution and can be used as program modules-standardized and re-useable throughout your organization. We will examine macros and macro varia bles, statements, and functions. We will intr oduce interaction b e- tween macros and regular SAS language, offer tips on debugging, and the SAS macro options | macro variable, macro facility, macro definition, macro loop, macro call, do loop, title statement, macro put, macro function, %if-%then-%else | source | 10 |
| 24 | A Week in the Life: A Visual Analysis of Internet Use by School-Age Students | Simon J. King, Aaron Daniels, Jacob Warwick |
This study took data of the Internet web hits for one week for the students at Cary Academy, a grades 6-12 independent public school in Cary, North Carolina. All students have their own school-issued tablet PC. Social networks are blocked while students are in school. Student web hits were monitored from 8 a.m. to midnight for a school week. It was discovered that student Internet use was up to twice as frequent outside of school, with the use of social network sites increasing greatly for upper-school students, with only a small increase for middle school students. All students showed a significant use of "streaming" websites, a more recent Internet trend. Conclusively, Internet use is not "out of control" in the classroom | moving average, sample, proc step | source | 12 |
| 25 | Accessing and Extracting Data from the Internet Using SAS | Qiaohao Zhu, Sunita Ghosh |
This paper shows you how to use SAS R⃝ to automatically access web pages from Internet and extract useful information from the web pages. We first review some basic elements of Internet accessing, then review the accessing and extraction tools provided by Base SAS. When the SAS tools are not enough or too complicated to use, we show how you can use external programs (cURL and Perl/LWP) and integrate the results from external programs into a SAS data set. Finally, we present 4 different examples to illustrate how to use these tools to deal with different levels of complexity for web accessing and data extraction | filename statement, extract | source | 10 |
| 26 | Adding Count as a Data Label in a Scatter Plot | Suneetha Puttabasavaiah | When there are too many data points in a scatter plot, it is useful to have a count label instead of showing all of the overlapping data points. Here are the simple steps to add a statistic as a data label to a graph: 1. Get the required statistic values for variable 1 and 2. 2. Output the required statistic values to a data set. 3. Sort the original data set by variable 1 and 2. 4. Merge the statistic values with the original data set. 5. Use the Datalabel option in SGPLOT to show the statistic as a label |
scatter plot, proc freq | source | 4 |
| 27 | Advanced XML Processing with SAS 9.3 | Thomas W. Cox | XML has expanded far beyond the scope originally envisioned by its creators, and this has resulted in the addition of companion standards such as XML Namespaces and XML Schema. This paper describes how SAS has advanced our XML technology in SAS 9.3 to more fully support these standards. It also examines some of the challenges you might encounter when processing complex XML and describes some best practices to help overcome the challenges | libname engine, proc step, proc sql | source | 11 |
| 28 | After Analytics, Whats Next? | Ping Shung Koo, Murphy Choy |
After gathering great insights from our data, the next steps would be devising and executing the strategy based on these insights. Strategy planning and execution can be seen as both an art and a science. Without proper planning and execution, these insights that we gathered would be meaningless. Many frameworks have been devised to help businesses in their strategy planning and execution. But most of them have generally been used at corporate level; for instance Balanced Scorecards. Here we introduce a framework, adapted from an ancient treatise of war, "Sun Zi Art of War," that can prov ide a strong framework at the strategy planning and execution stage so that analysts can assess the feasibility of their devised strategy | source | 4 | |
| 29 | An Advanced, Multi-Featured Macro Program for Reviewing Logs | Chris Swenson | An important, time-consuming task for any level of SAS user is reviewing the log for issues. Further, if there is no notification or plan in response to the notification, essentially no review occurred. There are several macros that check the SAS log for issues, and here we present a new macro program that adds several new features to the existing paradigm that assist with responding to issues. The macro includes capabilities to check internal and external SAS and logs not from SAS; to include keywords and/or exclude phrases from the search; to modify the notification behavior using a pop-up message, email, and/or sound; to "recreate" the log in the current interactive SAS session for review; and to abort the program should issues arise | sas log, macro variable, type i error, proc sql | source | 7 |
| 30 | An Automatic Approach to Creating a Data Dictionary in SharePoint Using SAS | Kevin Chung | You might have several data sets created by SAS applications and want to share the info rmation about these data to users. This paper illustrates how to use SAS to create a data dictionary for any existing SAS data sets and store the result in a form of html file in the Microsoft SharePoint. A data dictionary is a collection of information about data such as name, attribute, definiti on, source, data type, and length. SharePoint is a web application platform developed by Microsoft. It's widely used by divisions/departments in an ent erprise as a portal to communicate with internal and/or external users. By creating a data dictionary and making it available on SharePoint, new and old users will have a single location to pull descriptive attributes about fields for data sets quickly and efficiently | proc freq, data dictionary, macro variable, excel, dataset, proc univariate, univariate analysis, length statement, macro loop, data step | source | 17 |
| 31 | An Experimental Comparison of Classification Techniques for Imbalanced Credit Scoring Data Sets Using SAS Enterprise Miner | Iain L. Brown | In this paper, we set out to compare seve ral techniques that can be used in t he analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observati ons that do not default. As well as using traditional classification techniques such as logistic regression, neural networks and decision trees, this paper will also explore the suitability of gradi ent boosting and memory based r easoning (k-NN) in SAS Enterp rise Miner for loan default prediction. Five real-world credit scoring data sets are us ed to build classifiers and test their performance. In our experiments, we progressively increase class imbalance in each of these data sets by randomly under-sampling the minority class of defaulters, so as to identify to what ex tent the predictive power of the respective techniques is adversely affected | credit scoring, gradient boosting, proc hpsplit, imbalanced data, discriminant analysis, logistic regression, logistic distribution, friedman test, roc curve, sample, regression analysis, neural network, linear discriminant analysis, quadratic discriminant analysis, test set, data mining, validation set, proc step, data cleaning, response variable | source | 14 |
| 32 | An Introduction to Creating 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, sas output, output delivery system, sas base, proc print, proc step, ods xml, proc tabulate, sample | source | 20 |
| 33 | Analysis of Clickstream Data Using SAS | Sumit Sukhwani, Goutam Chakraborty, Satish Garla |
Analyzing Web data has become a must-have for businesses. Significant research has been done in studying clickstream data to understand the navigation behavior of users after visiting a Web site. Analyzing clickstream data is not easy for most companies because Web logs are stored in a form that is not suited for analysis. Before any meaningful analysis can be done, much effort is spent in transforming server logs to the right form so that they can be analyzed. This is one of the reasons why companies often use third-party services (such as Webtrends, Adobe, or Google Analytics) to analyze their Web log data. This paper demonstrates applying SAS macro programming to prepare a SAS data set from raw Web logs and to generate summary reports | dataset, sample | source | 13 |
| 34 | Analyzing Sentiments in Tweets about Wal-Marts Gender Discrimination Lawsuit Verdict Using SAS Text Miner | Hari Hara Sudhan Duraidhayalu, Satish Garla, Goutam Chakraborty |
Social Media has gained considerable attention as a valuable source to monitor customers and public reactions following corporate events. Especially the Tweets posted on Twitter are often used to spot trends, moods and sentiments of customers and public. Given the huge volume of tweets that gets posted every day, it is extremely difficult for firms to spot current trends related to public's expressed sentiments about activities of the firm in the tweets. This paper demonstrates the application of a SAS macro (%GetTweet) to collect and summarize tweets and then an application of sentiment analysis on the fetched tweets using SAS Text Miner using directed search and summarization of specific text items | proc freq, dataset | source | 8 |
| 35 | Analyzing Survival Data with Competing Risks Using SAS Software | Guixian Lin, Ying C. So, Gordon Johnston |
Competing risks arise in studies when subjects are exposed to more than one cause of failure and failure due to one cause excludes failure due to other causes. For example, the cause of failure in bone marrow transplantation can be relapse, death in remission, or other causes. The standard Kaplan-Meier method for survival analysis does not yield valid results for a particular risk if failures from other causes are treated as censored. A useful quantity is the cumulative incidence function, which is the cumulative probability of failure from a specific cause over time. The SAS macro %CIF implements appropriate nonparametric methods for estimating cumulative incidence functions. The macro also implements Gray's method (Gray 1988) for testing differences between these functions in multiple groups. This paper discusses these methods and illustrates the macro | hazard function, competing risks, confidence interval, cause-specific hazard, survival function, cumulative hazard | source | 8 |
| 36 | Analyzing the Time Series of U.S. E-Commerce Using PROC UCM | Anders Milhøj | In this paper PROC UCM is applied in an analysis of the series of E-Commerce which is published by the U.S. Census Bureau. This rather new procedure decomposes a time series into intuitive components such as levels, trends and seasonality which are easily specified in the SAS code. The advantages of PROC UCM as an easy-to-use alternative to a careful econometric analysis will be of focus in the presentation. The results are mainly presented by the graphical output which adds to the attractiveness of the procedure. Also more advanced features of PROC UCM will be applied such as a discussion of how the seasonality have changed and various ways to include the total retail sales as an independent variable in the model |
proc ucm, proc step, predictor variable, regression analysis, seasonality, estimate, time series analysis, proc calis, proc reg | source | 11 |
| 37 | Another Way to Use SAS to Monitor SAS or a SAS Server: A Tool for the User, Server Administrator, or Manager | LeRoy Bessler, Victor L. Andruskevitch |
The administrator or manager of a SAS server, whether BI or non-BI, has questions. Who is using SAS now and in the past, and how much of its resources (CPU, memory, or I/O)? How heavily does each use the server in terms of frequency or resources? The user, whether SAS is running on a remote server or on his/her own PC, has questions. What SAS processes do I have running? What resource consumption is making my process run so long? By tapping into a no-additional-expense technology built into Microsoft Windows, this paper enhances the UserMon tool presented in 2009. The CPUmon tool presented in 2010 could also be enhanced to send real-time email alerts for overloads on resources other than processor time. The tool presented here in 2012 can be adapted to also or to instead monitor any application other than SAS | sas log, dataset, horizontal bar chart, bar chart, sas enterprise guide, sas administrator | source | 10 |
| 38 | Application of Time Series Clustering Using SAS Enterprise Miner for a Retail Chain | Karthik Nakkeeran, Satish Garla, Goutam Chakraborty |
Much of the data that are generated in the operational side of a business have a built -in time dimension. One of the challenges of doing data mining using such time -series data is the complexity of handling a large number of time series. Time series clustering provides a w ay to reduce the complexity by categorizing large number of time series into a smaller subset such that series within each subset are relatively homogenous but series between subsets are heterogeneous. SAS has recently introduced new nodes for finding sim ilarities between the time series and to forecast their future trajectories. In this paper, we demonstrate clustering of store -level revenue over time and how profiling of such clusters generate additional business insights | time series analysis, time series data, data mining, transaction data, proc step, proc tree | source | 9 |
| 39 | Applications of PROC GEOCODE and Incorporation of Census Block-Level Data | John Havens-McColgan | PROC GEOCODE is a powerful tool in SAS with applications in epidemiological studies that involve geographically restricted exposures and health outcomes. This paper describes the use and application of PROC GEOCODE, specific to street-level geocoding. Preliminary steps are reviewed, and results are presented with a focus on applications of PROC GEOCODE in an observational study for which data at a census block level must be incorporated. Applications discussed include determining eligibility where requirements for the study are geographically limited, predicting the size of a population of eligible subjects in a geographic area, analyzing subject demographic characteristics, and comparing information to census data to ensure representative sampling |
proc step, reference data | source | 5 |
| 40 | Applying Analytics to High Performance Customer Profitability Models in Financial Services | Tony Adkins, Gary Cokins |
Where financial services companies have looked at customers in the past, much of the literature focuses on the "apex" customers. These are the top 10-50 customers by value, whose importance to the business means that they demand an extraordinary service, often at the expense of profitability. Historically, it was difficult to build models of a scale that produce customer profitability, so models tended to stop at segment level. We have tended to rely on the traditional rather arbitrary groupings used within a business, and this can disguise important trends. Bringing together analytics and high performance in memory profitability models allow us to discover hidden patterns in behaviors that cross arbitrary boundaries in the financial services industry and refine our interpretation of intermittent risks | proc hpsplit, customer profitability, cluster analysis, credit card, regression analysis | source | 9 |
| 41 | Applying Business Analytics to Optimize Clinical Research Operations | David Handelsman | SAS is widely accepted as the gold standard for determining safety and efficacy for clinical trials, and it provides the primary mechanism for preparing data for traditional clinical research analysis activities. However, most SAS users in the biopharmaceutical industry are unaware of the broad range of SAS analytics that are widely applied in other industries. This paper discusses and describes how SAS business and advanced analytics can be used to design better trials, forecast patient-based activities, and optimize other operational processes. Applying business and advanced analytics to clinical trial operations represents a new and improved approach to reducing the cost and time associated with managing clinical research projects. As a result, the roles of SAS experts in the biopharmaceutical industry are expanded | clinical trial, clinical research, data cleaning, likelihood function, life sciences | source | 8 |
| 42 | Are You Awash in the Sea of Competitive Price Intelligence? Let Analytics Be Your Life Raft | Alex Dietz, Natalie G. Osborn, Tugrul Sanli |
Now, more than ever, your guests have access to an ocean of price intelligence on the Web and are using your rates in context of your competitors to make their buying decisions. As a result, your competitor's rates can have a direct impact on the demand for your hotel. So you collect the data on your competitors' rates. But, hotel pricing is hard enough without struggling to account for your competitor's price strategy. Over-react to your competitors pricing and you can trigger a price war, under-react and be caught in a spiral of decreasing demand for your hotel. This paper describes how to leverage analytics with your competitor rate data to make better pricing decisions for your hotel | revenue management, price elasticity | source | 5 |
| 43 | Are You in Need of Validation? Psychometric Evaluation of Questionnaires Using SAS | Eric P. Elkin | Presentations at prior SAS user group meetings have focused on factor analysis and related topics in order to develop the scale structure of a questionnaire. Instead, this presentation will assume that the scale has already been developed but needs validation as a new scale or for use in a new population. The examples are taken from health- related quality-of-life research and will follow the "Guidance for Industry" published by the FDA (Dec. 2009). The focus will be on the classical test theory approach to psychometric validation including internal consistency, test- retest, and inter-rater reliability; construct and known-groups validity; and responsiveness. The discussion will include samples of SAS code as well as tips for interpreting and presenting results | correlation, effect size, data cleaning, standard deviation, anova, sample, factor analysis, proc step | source | 9 |
| 44 | Assigning a User-Defined Macro to a Function Key | Mary F. O. Rosenbloom, Kirk Paul Lafler |
Are you entering one or more of the same SAS Display Manager System (DMS) commands repeatedly during a session? The DMS offers a convenient way of capturing and saving frequently entered commands in a user-defined macro, and then saving the macro as a function key of your choosing. Are you typing SAS code for data exploration during program development or validation, only to delete it soon afterwards? If you are, then this code can be placed in a macro, too, and assigned to a function key. This paper illustrates the purpose and steps that you use to assign a user-defined macro to a function key | proc step, macro variable, data cleaning, macro definition, numeric variable, proc sort, proc univariate, macro call, sas output | source | 10 |
| 45 | At the Crossroads: How to Decide on Your Graphics Path | Mike Kalt, Cynthia L. Zender |
Have you heard about ODS Graphics or ODS Statistical Graphs and wondered what they mean to you? Do you have legacy SAS/GRAPH programs and wonder whether there's any benefit going down the ODS Graphics path? Are you at the graphics crossroads and uncertain which way to go? With the ODS Graphics capability as part of Base SAS in SAS 9.3, many users find themselves at the crossroads, trying to decide what path to follow-the traditi onal SAS/GRAPH path or the new ODS Graphics path. This presentation focuses on helping you make an informed decision. Although some concrete examples are shown, this presentation is aimed at the SAS programmer or SAS Enterprise Guide user who wants to know more before they set off down the graphics highway |
proc step, proc sgpanel, bar chart, proc sgplot, sas base, proc greplay, scatter plot, box plot, proc gchart, sas enterprise guide | source | 16 |
| 46 | At Your Self-Service: One Organizations Journey from Mainframe Reporting to Enterprise Business Intelligence | Cheryl Sivertson | The University of Victoria's Project NOVA replaced a number of technologically dated stand-alone systems with a state-of-the-art information technology infrastructure that integrates Student, Human Resources, Finance/Accounting, Alumni, and Facilities Management components. The change in infrastructure required the replacement of hundreds of mainframe reports that supported a variety of operational and strategic reporting processes. To meet mandatory British Columbia provincial government accountability requirements, the first priority was to replace the university's Student Enrollment Management reporting, which provided a rare opportunity to re-evaluate, re-invision, and re- structure all student-reporting processes. This paper discusses the journey from product selection to production SAS Enterprise BI Server Web portal implementation and every step in between. Challenges faced, benefits achieved, tips learned, and future steps are presented | business intelligence, phase i, data warehouse, phase ii, dataset, sas enterprise guide, phase iii, production run | source | 10 |
| 47 | Automagically Herding 101 SAS Users from Microsoft Active Directory to SAS Metadata | Stephen M. Overton | Creating users within SAS metadata can become a tedious task for the system administrator, especially in large environments with strict compliance policies around user access to data through SAS. This paper will discuss a process and provide code to import users from Microsoft Active Directory into SAS metadata. A stored process will be the graphical interface for the security administrator to pick-and-choose what users will be imported. This process can be extended further to import group memberships. This paper will only focus on importing users. Using a stored process to selectively import users from Active Directory streamlines user creation in SAS metadata and provides a point and click method to minimize human error | macro variable, proc sql, proc step | source | 8 |
| 48 | Automated Process for Routine Clinical and Safety Data Monitoring Reports: An Integration of EDC Database, Microsoft Outlook, and SAS | Songtao Jiang, Hsini T. Liao |
In the pharmaceutical or the medical device industry, routinely reporting clinical and safety data is required to meet the ICH Guideline for Good Clinical Practice. However, the repeated report production per se is sometimes time - consuming and requires a sign ificant amount of resources. Therefore, an innovation for improving the current reporting process is always a demand. An automated process leveraging the integrating features of an electronic data capture (EDC) database, Microsoft Outlook, and SAS is propo sed. Specifically, a completely data -driven process for generating routine reports by using Visual Basic Application (VBA) codes incorporated with moderate SAS coding is deemed cost-effective. The process can be applied to any industry where a programming professional is equipped with Microsoft Outlook and SAS as a working platform | gcp, clinical trial | source | 6 |
| 49 | Automatic Consistency Checking of Controlled Terminology and Value Level Metadata between ADaM Datasets and Define.xml for FDA Submission | Xiangchen (Bob) Cui, Min Chen |
When submitting clinical study data (SDTM and ADaM data sets) in electronic format to the FDA, it is preferable to submit data definition tables (define.xml) and a reviewer guide (define.pdf). It is desirable to ensure the consistency between data sets and define files, and achieve technical accuracy and operational efficiency. This paper introduces a SAS macro approach to automate consistency-checking of controlled terminology and value level metadata between ADaM data sets and define.xml. It avoids the waste of time and resources for verification of the consistency and/or resolution of inconsistency at a later stage. It also details how to develop ADaM Metadata (programming specification) for automation purpose, illustrates five scenarios of mismatches from consistency checking, and provides corresponding resolutions to these mismatches | controlled terminology, adam dataset, dataset, sdtm domain, bds, macro definition, time variable, cdisc | source | 15 |
| 50 | Automation of Paper-Based Medical Surveys Using the SAS BI Platform Custom Web Forms for Real-Time Analysis | Ian P. Healy, Rocket Wong |
Maine Medical Center has traditionally relied on paper-based surveying and inspections in order to determine the level of patient satisfaction and safety on a regular basis. The ability to generate substantial business intelligence using paper surveys was both labor-intensive and proved difficult to convey the results to the hospital community. The Center for Performance Improvement - BI Competency Center worked to bridge this gap by developing custom web forms using the SAS BI Platform. These forms have eliminated most paper surveying and resulted in ease of data entry, instantaneous feedback as well as real-time reporting and dashboards to assist in efforts for continuous improvement at Maine Medical Center | business intelligence, dataset, proc step, proc append, proc sql, excel | source | 10 |
| 51 | Batch Production of Driving Distances and Times Using SAS and Web Map APIs | Ash Roy, Yingbo Na |
This is a new methodology of using SAS URL access method and Web APIs to run queries on an interactive Web site. This method will capture driving distances and times from a Web map based on points marked by postal codes | out=, web service, ods xml | source | 7 |
| 52 | Becoming the Smartest Guys in the Room: An Analysis of the Enron Emails Using an Integration of Text Analytics and Case Management | John York, Doris Wong, Daniel Zaratsian |
By integrating SAS Enterprise Case Management with SAS Text Analytics, fraud investigators gain an analytical advantage. SAS Enterprise Case Management expedites fraud investigations by providing an organized environment for managing investigation workflows, documentation, and case notes. Using SAS Text Analytics, you can index and categorize each document, further enhancing search capabilities, uncovering root causes, and saving time and money during time-sensitive investigations and audits. The Case Management repository is valuable in itself, not only as an organizational repository, but also as a source of knowledge with alert capabilities that can be investigated with analytics in its own right. The integrated capability of utilizing SAS Text Analytics with SAS Enterprise Case Management is illustrated using the Enron email corpus | case management, unstructured data | source | 8 |
| 53 | Beep, Beep, Beep, Back It Up! A Fool Proof Approach to Archiving with no Copying | Kristen Reece Harrington | Have you been asked to revise a derivation and then several months later asked to revert back to the previous derivation code? Or when a project ends, were you asked to remove it from the network to free up space? What if Archival Software is not an option? This paper presents an "archiving" macro (%Archive()) which takes the contents of a directory, including subdirectories, and creates a zip file which contains all files and mirrors the parent's directory structure. Like magic, your code is saved and easily retrieved! Using an elaborate array of SAS functions, an X command, and WinZip, the archiving macro determines the execution location and the contents of the subdirectories associated with that location. With virtually no specified parameters, anyone can archive and look like a pro while doing it | macro variable, macro loop | source | 3 |
| 54 | Best Practices for Administering SAS Enterprise Guide | Casey Smith | Customers and clients just want it to work. Management wants it to be secure and "intuitive" while sliding you a list of ten new unrelated "action items" needed, "when you aren't busy". You are in the middle, the grease between the cogs. When your phone rings, it isn't going to be someone gushing about how smooth everything is running. Sound familiar? If part of your work is administering and supporting people using SAS Enterprise Guide, then don't miss this talk. We cover deployment issues, environment customization, handling libraries, roles, SAS Grid Manager support, migration, and project maintenance. Come learn about the SAS Enterprise Guide tools designed to make administrative life just a bit easier | sas enterprise guide, libname statement, sas management console, sas grid, parallel processing | source | 21 |
| 55 | Best Practices for Managing and Monitoring SAS Data Management Solutions | Gregory S. Barnes Nelson | SAS and DataFlux technologies combin e to create a powerful platform for data warehousing and master data management. Whether you are a professional SAS administrator who is responsible for the care and feeding of your SAS architecture, or you find yourself playing that role on nights and weekends, this paper is a primer on SAS Data Management solutions from the perspective of the administrator. Here, we will review some typical implementations in terms of logical architecture so that you can see where all of the moving parts are and provide some best practices around monitoring system and job performance, managing metadata including promotion and replication of content, setting up version control, managing the job scheduler, and discuss various security topics. Data ManagementSAS Global Forum 2012 2 | sas data integration studio, sas administrator, sas batch, master data management, life sciences | source | 13 |
| 56 | Best Practices: Clean House to Avoid Hangovers | Mary F. O. Rosenbloom, Kirk Paul Lafler |
In a production environment, where dozens of programs are run in sequence, often monthly or quarterly, and where logs can span thousands of lines, it's easy to overlook the small stuff. Maybe a DATA statement fails to execute, but one already exists in the Temp library from a previous program. Maybe a global macro assignment is missed or fails to execute, but a global macro of the same name already exists from a previous program. This can also happen with macros. The list goes on. This paper offers some suggestions for housekeeping that can be performed at the end of each SAS program to minimize the chance of a hangover |
options statement, macro variable, temporary dataset, proc step, dataset, sas log | source | 7 |
| 57 | Better, Consistent Customer Experience from Analytically Based Real-Time Decisions | Toshi Tsuboi | Consumer expectations from their interactions with business have increased tremendously. Today, they expect that businesses, armed with technology and customer databases, should be able to provide a relevant, consistent customer experience across all channels. Unfortunately, this is not often the case because data is underutilized, business processes are disjointed, and analytics are not leveraged to make the optimal decisions. As a result, customer loyalty and revenue opportunities are lost. This presentation describes how organizations can use SAS products, including SAS Real-Time Decision Manager and SAS Model Manager, to apply SAS analytics to improve customer experience by providing real-time analytic-based decisions to channels and business processes that drive consistent, personalized customer experiences. This presentation provides a how-to overview of the solutions and utilization examples | sas model manager, big data, customer experience, likelihood function, data mining | source | 8 |
| 58 | Beyond Binary Outcomes: PROC LOGISTIC to Model Ordinal and Nominal Dependent Variables | Eric P. Elkin | The most familiar reason to use PROC LOGISTIC is to model binary (yes/no, 1/0) categorical outcome variables. However, PROC LOGISTIC can handle the case where the dependent variable has more than two categories. PROC LOGISTIC uses a cumulative logit function if it detects more than two levels of the dependent variable, which is appropriate for ordinal (ordered) dependent variables with 3 or more levels. A generalized logit function for the LINK= option is available to analyze nominal (un-ordered) categorical variables with 3+ levels (i.e., multinomial logistic regression). Detailed examples will be given, emphasizing procedure syntax, data structure, interpretation of statistical output, and ODS output data sets |
response variable, logistic distribution, proc logistic, logistic regression, proc step, odds ratio, regression analysis, likelihood function, maximum likelihood estimation, nominal variable, predictor variable, ordinal variable, null hypothesis, multinomial logistic regression, sample, binomial distribution | source | 8 |
| 59 | Beyond Star Schema: Exploring the Next Query Generation with the SAS Enterprise BI Server | Fred Levine | Structured Query Language (SQL) generation is an essential function of the SAS Enterprise BI Server that enables consistent, self-service access to data for many users. This paper explores enhanced ways that the BI server generates "intelligent" queries with SAS 9.3. The new Intelligent Query generator generates SQL based on complex dimensional and relational data models, while providing users with an easy and unique way of asking very specific questions about their data | fact table, data standard, star schema, dimension table, proc sql, proc step, sample | source | 6 |
| 60 | BI at Your Fingertips: Creating Immersive Mobile Reports with SAS Visual Designer | Scott W. McQuiggan, David A. Coyle, Gregory M. Thorne, Philippe P. Sabourin |
The pervasiveness of mobile computing has created a paradigm shift in how information is consumed. It has become increasingly evident that report consumers expect business intelligence to be readily available, timely, and easily accessible wherever they are located. The new BI pipeline from SAS facilitates the distribution of information to the fingertips of decision makers anywhere. In this paper, we offer best practices, tips, and tricks for creating compelling, immersive reports for mobile BI users. Learn how to transform a Web report into a mobile report using the SAS Visual Analytics designer interface and how to take advantage of features in the SAS Mobile BI app to delight and empower decision makers anywhere | source | 9 | |
| 61 | BIG Money PROC TABULATEGenerating Great Reports with the TABULATE Procedure | Ben Cochran | Several years ago, one of my clients was in the business of selling reports to hospitals. He used PROC TABULATE to generate part of these reports. He liked the way this procedure 'crunched the numbers', but not the way the final reports looked. He said he would go broke if he had to sell default PROC TABULATE output. So, he wrote his own routine to take TABULATE output and render it through Crystal Reports. This was in during the days of the 6.12 release of SAS before there was something called the Output Delivery System (ODS). Once he got his hands on SAS ODS, he kissed his Crystal Reports good-bye. This paper is all about using PROC TABULATE to generate fantastic looking reports. If you want to generate BIG money reports with PROC TABULATE, this presentation is for you |
proc tabulate, proc step, output delivery system, class variable | source | 12 |
| 62 | Black Box PROCs: PROC LOGISTIC Discovered | Isabel H. Perry | Black box procedures are incredibly useful for the modern statistician, but they might not be aligned to what we think is happening behind the scenes. Students might find themselves consulting many resources to ensure that the code that they have just copied and pasted from an Internet source is performing the theory that they have just learned from their textbook. This paper combines statistical theory and the SAS code needed to implement a binary logistic regression analysis using health care data | logistic distribution, logistic regression, regression analysis, proc logistic, proc step, predictor variable, response variable, goodness of fit, null hypothesis, odds ratio, proc reg, feature selection, likelihood function, main effect, binary variable | source | 7 |
| 63 | Bringing Optimization to the Business: Interfacing SAS/OR with SAS Stored Processes, Microsoft Excel, SAS Enterprise Guide, SAS Forecast Studio, and Microsoft Project | Andrew S. Pease | SAS/OR software provides a powerful array of optimization, simulation, and project scheduling techniques to identify the actions that will produce the best results, while operating within resource limitations and tight restrictions. This paper focuses on ways to get this information to business users in an intuitive and interactive way. Via SAS Stored Processes, users can change parameters and run different configurations of a base SAS/OR program. These stored processes can be surfaced via the SAS Add-In for Microsoft Office, SAS Enterprise Guide, and SAS Forecast Studio. Also, via Microsoft Project macros, Microsoft Project users can export parameters to SAS/OR, which produces optimized planning, and then push back to the Microsoft Project user. The technical setup of these various channels is explored in demonstrations |
sas enterprise guide, macro variable, proc step, odds ratio, excel | source | 15 |
| 64 | Building an Optimal Execution Plan for Liquidity Management Using SAS | Wei Chen, Liping Cai, Jimmy Skoglund |
Liquidity risk management is the management of the bank's ability to meet its obligations as they come due, w ithout incurring losses. Liquidity ris k management is seen to be of paramount importance and a subject of great interest for the regulators because a liquidity shortfall at a s ingle significant institution can lead to system-w ide effects. In contrast to risk based capital for other forms of risks such as market and credit risk, the cushion for liquidity ris k is not created through additional capital. Since the main purpose of the cushion for liquidity risk is to mitigate the n et cumulative cash outflows, it is done by us ing a pool of high -quality liquid assets that can be sold immediately or used in collateral for short -term loan (repo) transactions to raise funds. Given a sufficient liquidity hedging portfolio banks also need to consider strategizing its response to liquidity crisis in advance. Most notably, this includes having a strategy for liquidity execution | stage 1, stage 2, liquidity risk, stress scenario, liquidity coverage ratio, basel iii, liquidity gap, net stable funding ratio, credit risk, sas risk, stress testing | source | 8 |
| 65 | Building WOE Binned LGD Scorecards using SAS Enterprise Miner | Anthony J. van Berkel, Naeem Siddiqi |
The Credit Scoring add-on in SAS Enterprise Miner is widely used to build binary target (good, bad) scorecards for probability of default. The process involves grouping variables using weight of evidence, and then performing logistic regression to produce predicted probabilities. Learn how to use the same tools to build binned variable scorecards for Loss Given Default. We will explain the theoretical principles behind the method and use actual data to demonstrate how we did it. Loss Given Default Loss Given Default (LGD) is defined as the loss to a lender when a counterparty (borrower) defaults. It is one of the key parameters banks need to estimate for measurement of credit risk under the Internal Ratings Based approaches specified by the Basel II accord - the other 2 measures being probability of default (PD) and Exposure at Default (EAD). LGD is normally estimated as a continuous variable that predicts the ratio of loss to balance at the time of default, with a range of 0 to 1 | lgd, data mining, basel ii, regression analysis, proc hpsplit, logistic regression, logistic distribution, data cleaning, predictor variable, sample, interval variable, credit risk, credit scoring | source | 14 |
| 66 | Calculating Multi-Rater Observation Agreement in Health Care Research Using the SAS Kappa Statistic | Abbas S. Tavakoli, Bo Cai, Rita Snyder, Nathan Huynh |
This paper describes procedures for calculating multi-rater observation agreement using the SAS Kappa statistic in a health care research study of the medication administration process (MAP). In the study, Registered Nurses (RN s) modeled the oral MAP 27 times in a simulated laboratory env ironment. All 27 MAP modeling session were video recorded. Four individuals observed each of the 27 modeling sessions and recorded the MAP functions and tasks that they observed for each session. Three nurse subject matter experts (observers 1, 2 and 3) watched video recordings of the 27 MAP sessions and recorded their observations on a paper Video Observation Scoring Form. The fourth observer (observer 4) watched all 27 MAP sessions in real -time in the simulation laboratory and recorded his observations on a hand-held device. The content on the scoring form and hand -held device was identical | cohen's kappa, proc freq, proc step, sas syntax | source | 11 |
| 67 | Case Study: Implementing and Administering SAS Enterprise Guide across the Enterprise as a Solution for Data Access Security | Linda Sullivan, Ulf M. Borjesson, Evangeline Collado, Maureen Murray |
This paper presents a case study detailing why and how the SAS Enterprise Guide client was chosen, approved by UCF's security office, and rolled out to selected power users and unit IT shops in the university community as the solution to replace two distinct data access and reporting tools. The selection increased the number of SAS Enterprise Guide users across the university by a factor of five | sas enterprise guide, data mart, proc sql, business intelligence, data warehouse, excel | source | 4 |
| 68 | Classification of Customers Textual Responses via Application of Topic Mining | Anil K. Pantangi, Goutam Chakraborty |
Customer satisfaction surveys play a vital role in monitoring business performances in most industries. Typically, customer satisfaction surveys are a blend of closed-end questions (numeric responses) and open-ended questions (textual) leading to a collection of structured and unstructured data. Textual comments are generally cumbersome and time consuming to summarize and analyze. Sometimes, businesses use experts to rate unstructured data to understand the nature and the valence of customers' responses. In this paper we illustrate the application of Topic Mining, using SAS Text Miner; to first categorize customers' responses collected via a survey of customers of a B2B (business-to-business) company. Then, we use the topics to build a predictive model to automatically classify the responses into negative versus non-negative categories. The best predictive model had a sensitivity of 93% and a misclassification rate of 12% in the validation data | customer satisfaction, validation set, data mining, data cleaning, logistic regression, unstructured data, regression analysis, logistic distribution | source | 6 |
| 69 | Cluster Mapping of Medicare Severity Diagnosis-Related Groups for ICD-10 Conversions | Keith A. Jones | Diagnosis Related Groups (DRG's) have been critical to Medicare procedure-based billing, prospective payment and risk management since it was first implemented. More recently, Med icare developed "Severity -DRG's (MS-DRG) to weight top factors to optimize cost, time, and clinical outcomes; and new rules for US Medicare claims processing . Now US Medicare provides Generalized Equivalence Map (GEM) clusters to help convert ICD-9-CM to ICD-10-CM in 2013, and better optimize billings in the future. Such mappings can be used by insurers to optimize clinical outcomes and costs, using SAS/STAT procedures and SAS Enterprise Miner, with reporting process automation by SAS Enterprise Guide, and visualization using SAS/GRAPH and with SAS/JMP, to build and clust er clinical diagnosis and procedu res for cost classification models. Examples are presented to support scaled SAS solutions to better comply with, and benefit from, these new Medicare resources |
clinical endpoint, sample, proc step, data mining, proc princomp, data governance, sas enterprise guide, health insurance, excel | source | 11 |
| 70 | Clustering Physicians Based on Professional Proximity Using SAS | Niam Yaraghi, Anna Ye du, Raj Sharman, Ram Gopal, Ramaswamy Ramesh, Ranjit Singh |
We show how SAS can be used to conduct a network analysis of physicians in order to identify the professional proximity of different specialties based on the common patients flow. The professional proximities can be used as a basis for further research in various topics such as adoption and usage patterns of Healthcare Information Exchange systems. We are building this paper on the previous paper by Zheng (2011) .In the current paper, we provide clearer codes and present the results based on real data, and we further expand the previous paper by showing the differences in Multi-Dimensional Scaling (MDS) analysis results when the number of dimensions increase. Moreover, we show how the outputs of MDS analysis can be used to conduct a cluster analysis | proc step, proc tree | source | 7 |
| 71 | Combine Data Sets Using Inexact Character Variables in SAS | Kulwant Rai | This project proposes an algorithm to combine datasets using inexact character variables, and implements this algorithm using SAS software. When this algorithm is used together with PROC SQL it allows combining the datasets based on several inexact variables, hence producing better matches. This procedure offers realistic solutions for accurate and more complete matching of inexact data fields. Apart from being simple and easy to understand, the strength of this procedure is to condense information from several inexact character variables into one numerical measure. Thus, many valid matches can be captured that previously could only be made with an individual's exhausting visual inspection. The procedure will save time and effort for anyone trying to combine datasets with inexact character variables |
character variable, proc sql, proc step, data merging, create table, group by, proc fcmp | source | 12 |
| 72 | Combined Forecasts: What to Do When One Model Isn't Good Enough | Ed Blair, Michael J. Leonard, Bruce Elsheimer |
SAS High-Performance Forecasting 4.1 offers a new, innovative process for automatically combining forecasts. Fore- cast combination, also called ensemble forecasting, is the subject of many academic papers in statistical and forecast- ing journals; it is a known technique for improving forecast accuracy and reducing variability of the resulting forecasts. By integrating these methods into a single software system, SAS High-Performance Forecasting 4.1 surpasses the functionality of any existing software system that incorporates this capability. This paper describes this new capability and includes examples that demonstrate the use and benefits of this new forecast combination process | feature selection, akaike information criterion, estimate, time series analysis, proc step, forecast horizon, residual, standard deviation, correlation, proc esm | source | 14 |
| 73 | Common Sense SAS - Documenting and Structuring Your Code | Gary Pool | Job security does not result from being the only one who can fix your programs. It results from being the one who is most productive and able to pass projects on to someone else. The most frightening thing that confronts a SAS programmer, whether amateur or professional, is to be presented with the challenge of debugging or updating someone else's code. Two simple things can make your code more valuable: effective documentation and good code structure. This paper shows the importance of documentation and offers suggestions on how to make it effective. It demonstrates simple techniques for creating clear, easy to read, easy to debug, and easy to update code | if-then-else, macro variable, proc step, macro definition | source | 7 |
| 74 | Community Detection to Identify Fraud Events in Telecommunications Networks | Carlos Andre R. Pinheiro | Telecommunications' industry evolves into a high competitive market which demands companies to establish an effective revenue assurance framework. Social network analysis can be used to increase the knowledge about the customers' behavior, not just in terms of individual usage but mostly in relation to the customers' connections and how they create communities according to their call and text messages. By performing community detection, telecommunications companies are able to recognize groups of customers which unexpected behavior in terms of usage and also in regard to types of social structures. Outliers groups might be pointed out as suspicious communities in terms of fraud events, delivering a relevant knowledge about possible leakages of money | social network analysis, outlier, proc freq | source | 8 |
| 75 | Community Discovery: Best Tips and Features from Communities on SAS | Renee Harper, Cynthia L. Zender |
Not long ago, "community" meant a collection of neighborhoods, grocery stores, and a place to gather and talk about life. Then, it all changed to include a gathering place on <gasp> the Internet. SAS recently upgraded our discussion forums to a new community format, making it easier for you to stay informed, share knowledge, grow your professional network, and get help. But how do you go about posting a question online? One key to successful community interaction is to learn how to ask your question rather than posting a riddle. We'll cover the basic netiquette of posting to the new community (or to any online forum). Come prepared for interactive and hands-on learning | proc step, hash table | source | 14 |
| 76 | Comparing Stock Returns Forecasting Methods Using SAS | Wei Wang, Arthur X. Li |
The accuracy of forecasting stock returns is the key component to generating profits on Wall Street. There are many methods to forecasting stock returns. Almost all the data mining methods are concerned with creating analytical models that are based on historical data trends. Choosing the best forecasting method is essential to obtaining fruitful stock returns. SAS provides a flexible platform that allows one to easily compare different forecasting methods. In this paper, we compare three most- commonly-used technical trading methods (Moving Average, Relative Strength Index, and Bollinger Bands) by using SAS based on historical stocks from 1980 to 2010. All the calculations are performed by utilizing the DATA STEP or SAS macros. The comparison generated from the program helps us to decide the best forecasting method |
moving average, sas output, standard deviation, sample | source | 8 |
| 77 | Comparison of K-Means, Normal Mixtures and Probabilistic-D Clustering for B2B Segmentation Using Customers Perceptions | Satish Garla, Gary Gaeth, Goutam Chakraborty |
Cluster Analysis is a popular technique used by businesses and analysts for market segmentation. For segmentation, clustering is used to split customers in a market into meaningful groups such that the customers within a group are similar and customers between the groups are dissimilar. Several clustering methods and numerous clustering algorithms are available in existing software packages and new ones frequently appear in the literature. These methods and algorithms vary depending on how the similarity between observations is defined or on other assumptions about shapes of clusters, distributions of variables, etc. This paper describes a comparative study of three clustering methods (K-means, Normal Mixtures and Probabilistic-D) for segmenting business-to-business (B2B) customers using their perceptions | data mining, cluster analysis, euclidean distance, outlier | source | 8 |
| 78 | Comparison of SAS Graphic Alternatives, Old and New | LeRoy Bessler | Since my first encounter with SAS/GRAPH software in 1981, I have tried to create communication-effective SAS graphics, rather than simply accept the defaults. This tutorial shows alternative customized SAS Version 9.3 graphic solutions, using both the traditional G procedures and the new SG (Statistical Graphics) procedures. It covers graphs written to disk to later be inserted into Microsoft PowerPoint, Word, or EXCEL, or simply printed, but also demonstrates the benefits of web-enabled SAS graphs, as well as web-enabled SAS graphs linked forward and backward with an EXCEL spreadsheet of the data graphed. A graph enables quick and easy inference, but the actual data assures correct inference. A spreadsheet of data can be post-processed however your information recipient wishes, using a tool available and familiar to almost anyone |
bar chart, time series analysis, proc step, proc greplay, output delivery system | source | 22 |
| 79 | Connect with SAS Professionals Around the World with LinkedIn and sasCommunity.org | Charles E. Shipp, Kirk Paul Lafler |
Accelerate your career and professional development with LinkedIn and sasCommunity.org. Establish and manage a professional network of trusted contacts, colleagues and experts. These exciting social networking and collaborative online communities enable users to connect with millions of SAS users worldwide, anytime and anywhere. This presentation explores how to create a LinkedIn profile and social networking content, develop a professional network of friends and colleagues, join special-interest groups, access a Wiki-based web site where anyone can add or change content on any page on the web site, share biographical information between both communities using a built- in widget, exchange ideas in Bloggers Corner, view scheduled and unscheduled events, use a built-in search facility to search for desired wiki-content, collaborate on projects and file sharing, read and respond to specific forum topics, and more | sample | source | 14 |
| 80 | Constructing a Credit Risk Scorecard Using Predictive Clusters | Andres F. Gonzalez, Alejandro Correa Bahnsen, Ana C. Nieto, Darwin A. Amezquita |
Traditionally the cluster analysis has been used as a descriptive tool, in w hich the algorithm is used to create groups of observa tions based on their characteristics . In this paper the use of cluster analysis as a part of a predictive algorithm is proposed. This methodology is applied by first determining to which cluster a prospect client belongs, and then calculate a specific credit risk scorecard for each cluster . Results will show that this approach provides better results than using a single scorecard for all the prospect clients | neural network, logistic regression, regression analysis, logistic distribution, credit risk, cluster analysis, f1 score, multinomial logistic regression, mahalanobis distance, credit score, response variable, network optimization, insurance risk score, data mining, predictor variable | source | 13 |
| 81 | Consulting: Critical Success Factors | Kirk Paul Lafler, Charles E. Shipp |
The consultants of today require different skills than the consultants of yesterday. Today's consultants are just as likely to have an MBA degree as a technical degree. They tackle a wide variety of business and technical problems and provide solutions for their clients. This presentation describes the consulting industry from the perspective of these different types of organizations (for example, Big Five accounting firms or organizations that could be described as elite, boutique, independent, or IT). Specific attention is given to the critical success factors needed by today's and tomorrow's consultants | sas 9, sas base | source | 8 |
| 82 | Convert Your Old Plots and Charts to New SG Plots and Charts: Here's How | Gabe Cano | It is definitely in your best interest to move your old SAS/GRAPH plots and charts to the new SG graphics suite. What was once a timeless exercise in producing quality graphs has now been replaced by a rich graphics suite environment. The SG graphics suite enables you to customize plots and charts with better context-driven graphics options. With a little information, you can quickly get started building or rebuilding your graph plots and charts. SAS continues to grow its Base SAS product into a business-level application that can better serve analysts and business users alike. This presentation shows two examples of how to move your old SAS/GRAPH plots and charts to the new SG graphics suite |
proc step, proc gplot, proc gchart, proc sgplot, scatter plot, proc sgpanel, regression analysis, sample | source | 13 |
| 83 | Converting Complex Microsoft Access Database Reporting Systems Using SAS Enterprise Guide | Scott A. Milbuta | The need to store more and more data related to aggregate enrollment information for use in the Registrar's Office at the University of Central Florida led to the development of several Access databases several years ago to assist in capturing this data. The Access databases were expanded and adapted to perform additional functions beyond their intended scope. Due to the amount of data stored and the delivery methods originally implemented they were no longer able to meet the organization's reporting needs effectively and a new solution would be required. The Registrar's Office with technical support and assistance from Enterprise Decision Support (EDS) was able to develop a new and more robust data collection and delivery system. This paper will explore the transition from existing silo data stores to the SAS Business Intelligence environment and how it was accomplished using SAS Enterprise Guide (EG) and Stored Processes on the University of Central Florida SAS Information Portal | sas enterprise guide, business intelligence | source | 6 |
| 84 | Copy and Paste Almost Anything | Arthur S. Tabachneck, John Henry King, Nathaniel Derby, Randy Herbison, Richard A. DeVenezia, Ben Powell |
Every day, data appear on computer screens in the form of spreadsheets, wiki pages, HTML, PDF, Word documents or any of the methods that are used to display data forms and tables. And, with any of those formats you can typically highlight and copy only the data you desire to your computer's clipboard. However, because there currently isn't a PROC IMPORT dbms=clipbrd option, how can you paste such data into a SAS dataset? The present paper provides code that we believe can be used to accomplish most such tasks and, at the same time, provides examples of features that we think should be available in PROC IMPORT for all DBMS options |
macro variable, proc import, proc step, missing value, dataset, label statement | source | 16 |
| 85 | Correlating the Analysis of Opinionated Texts Using SAS Text Analytics with Application of Sabermetrics to Cricket Statistics | Praveen Lakkaraju, Saratendu Sethi |
Cricket is a game similar to baseball. It is rich in statistics, and there are plenty of online discussions about the game and the players. Sabermetrics deals with using statistical methods to analyze baseball records. This paper presents the results of experiments with applying Sabermetrics-style principles to the game of cricket and correlating the findings with analysis of opinionated text. Examples use various products from the SAS Text Analytics suite to demonstrate how structured and unstructured data analysis can be applied together to gain insights into real-world data | key performance indicator, unstructured data, correlation | source | 10 |
| 86 | Create Multi-Sheet Excel Workbook for Large Data Sets by SAS and VBA | Chao Huang | With fast -growing data volume, transforming large data sets fro m SAS to multi -sheet Microsoft EXCEL workbooks becomes challenging. In addition, more than one grouping variables may be specified to separate a SAS data set to sheets in an EXCEL workbook. This paper describes a new and fast solution to create multi -sheet EXCEL workbooks, which also allows multiple grouping variables for each sheet. Two examples with SAS's help data sets will be used to illustrate how to use ODS HTML destination and a VBA script to produce EXCEL workbook. This two -step approach can process very large SAS data sets for multi-sheet EXCEL reporting in a short time. It is also customizable for special needs such as traffic lighting |
excel, xls, dataset, ods html, macro definition, proc step | source | 6 |
| 87 | Creating a Report in the SAS Information Delivery Portal Using SAS Information Maps | Jenine Milum | The purpose of this task is to guide a SAS BI developer through the steps necessary to combine SAS datasets using Information Maps and create a report which becomes available in the Information Delivery Portal. The result is a professional report viewable by the Business User within their browser. The ability to use the tools listed below to create a report available in the Information Delivery Portal frees Business Users from concerning themselves with data construction, security, information updates, and collection requirements. The Business User can focus on Analysis and other tasks without needing to be a SAS Developer. SAS Versions 9.2/4.2. Management Console Information Map Studio Web Report Studio Information Delivery Portal | sas administrator, dataset | source | 29 |
| 88 | Creating Pharmacokinetic Graphs Using SAS/GRAPH | Katrina E. Canonizado, Matthew Murphy |
Graphs are visual representation of the data. In a clinical trial setting, figures show the general trend and relationships of variables collected during the conduct of a study. In the pharmacokinetic world, graphs pla y an important ro le in showing the drug's concentration profi les. Line graph is a typical way of presenting drug's concentration. However, depending o n the data available and the analysis required, different visual presentations, such as scatter plots, spaghetti plots and many more, are needed to fully communicate the information at hand. These graphs can b e achieved by using SAS/GRAPH. This paper demo nstrates some available SAS/GRAPH procedures, options and the Annotate facility for producing effective pharmacokinetic graphs |
proc step, proc gplot, regression analysis, proc reg, sample, confidence interval, proc greplay, scatter plot, spaghetti plot | source | 8 |
| 89 | Creating the Perfect Plot with PROCs EXPAND and SGPLOT: The Dynamic Duo of Time Series Display | Andrew H. Karp | Many reporting and analysis applications require preparation and display of data values across or along a time horizon. These representations are often referred to as a "time series" or a "realization of a process in the time domain." The displays inform the reader about changes in the data values, suggest trends in the series over time, highlight seasonal changes in the series and might also show the impact of abrupt changes to the process under analysis, often referred to as "shocks" or "interventions." Effective display of time series data is not only an essential aspect of a reporting or analytics process, but is also the first step in developing statistical models to forecast future values of the series as a function of the prior observed data. But, many available time series need substantial manipulation before they are ready for display | time series analysis, proc step, time series data, proc sgplot, data wrangling, series plot, proc freq, missing value | source | 2 |
| 90 | CSI: Customer Segmentation Intelligence for Increasing Profits | Darius S. Baer | What will your customer do next? Customers behave differently; they are not all average. Can you simultaneously meet your customers' needs and improve profitability? You can by communicating and providing better offers to customers based on their behavior and demographics. Segmentation gives you the ability to group customers in many ways: 1) Business rules; 2) Supervised clustering - decision trees, and so on; 3) Unsupervised clustering; 4) Creating segments based on quantile membership. Which one works? That depends on two factors: the business goal and customer attributes. Do you want to increase share of wallet, market share, customer satisfaction, or all three? This presentation covers roles played by business issues and available data, and which SAS segmentation technique is appropriate for particular situations | data validation rules, proc hpsplit, proc freq, likelihood function, response variable, proc step, proc fastclus, customer satisfaction, customer segmentation, data mining, euclidean distance, predictor variable, proc princomp, outlier | source | 13 |
| 91 | Custom Analysis and Reporting with the JMP Application Builder | Daniel Schikore | The JMP Application Builder is a drag-and-drop environment for custom application development. Developers have access to the full capabilities of JMP, including more than 50 analysis and graphics platforms for in-memory analytics, as well as custom scripting with JSL to connect with SAS or R for more powerful techniques. Custom, multi-window reports can be created interactively without having to write scripts at all. A flexible, hierarchical data filter allows the developer to apply filters to all or parts of a report. The developer can deploy a JMP Application for other JMP users to run, optionally encrypting the application with a password required to run the application | dataset, sample | source | 12 |
| 92 | Custom Rollup: When the Cubes Default Behavior Doesnt Do the Right Job! | Paresh M. Patel | Aggregation tables are primarily used as a method of Cube data storage and enhanced query performance, but they can also be used to satisfy business queries by creating custom rollup. Aggregation tables are built to define data in the cube that is available to satisfy query requests without having to summarize the data on the fly. An important aspect of Cube design is cube granularity. When cubes roll up to incorrect numbers, it's simply wrong. 'Very wrong. In practice people like Business Managers can get surprisingly tense about this kind of error, and you end up getting aggravation about your aggregations.' This paper presents a practical challenge encountered when rolling up cube data with attributes that have a many-to- many relationship in a cube that was resolved using aggregated tables | sas enterprise guide, data validation rules | source | 11 |
| 93 | Customer Experience Modeling | Estelle Marianne, Nick Merry, Wendy Au |
Companies with large customer bases and high volumes of service interactions often have a number of customers who experience a service failure. The impact on these customers involved can be significant, and has the potential to be disproportional to the root cause of the incident. For complex and dynamic businesses, the continuous introduction of new products and services can create conditions where there are increased opportunities for suboptimal customer service outcomes. The authors have employed predictive analytics to provide the business with an opportunity to remediate these issues before the customer feels compelled to take action. Building robust sources of customer interaction data and an automated scoring environment has made it possible to deliver highly effective "customer dissatisfaction' scores to front of house. This provides a valuable "last chance' opportunity to solve serious customer service issues | customer experience, likelihood function, sample | source | 7 |
| 94 | Customizing ODS Graphical Output for SAS/STAT Procedures | Yang Xiao, Xiangxiang Meng |
Many SAS/STAT procedures can generate graphs based on ODS Graphi cs. However, in these statistical procedures there are few options available for the image configuration of the ODS graphs. In this pape r, we discuss three ways to customize the ODS graphical outputs f rom a statistical procedure including 1) using the built-in PLOTS= option in the procedure statement; 2) modify ing the Graphical Template Language (GTL) template used by the SAS/STAT procedures; and 3) using ODS style tem plate. The odds ratio plots and the ROC curves prod uced by the LOGISTIC procedure are used throughout the examples to introduce these three methods step by step |
logistic distribution, roc curve, odds ratio, proc logistic, proc step, proc template | source | 9 |
| 95 | Customizing SAS OQ to Provide Business Specific Testing of SAS Installations and Updates | Steve Huggins | The SAS Installation Qualification and Operational Qualification tools provide a generic inbuilt method of validating a SAS installation. This paper describes a simple method that allows the Operational Qualification tool to be customized to execute additional SAS code in order to provide a robust method for regression-testing a new SAS installation using business-specific SAS programs. The process involves selecting one or more indicative SAS programs, benchmarking the expected results, and then integrating the SAS programs into the Operational Qualification tool. This paper is aimed at SAS users and administrators wishing to upgrade or update their SAS installation whilst comprehensively ensuring the consistency of their results | sysrc, proc compare, proc step, data step, macro variable | source | 8 |
| 96 | De Rigueur - Adding Process to Your Business Analytics Environment | Falko Schulz, Diane Hatcher, Steve Sparano |
In an environment where there are multiple stakeholders involved in delivering business analytics, process is required to establish "rigor," a check-and-balance system where reports and other analytic assets must be verified and approved before being shared across the enterprise. Defining an approval workflow is a common mechanism for implementing this process. With the release of SAS 9.3 Integration Technologies, your SAS business analytics environment now includes the ability to define workflows. This paper describes how to set up a report approval process where reports are developed, submitted for approval, and then made available in production (if approved). This process is managed via a custom portlet that is made available through the SAS Portal, leveraging the new workflow capabilities to track progress and execute batch SAS code | spk file, sample, batch job, web service, sas batch | source | 22 |
| 97 | Deciphering PROC COMPARE Codes: The Use of the bAND Function | Joseph W. Hinson, Margaret M. Coughlin |
The COMPARE procedure is very useful for validating SAS data sets in clinical studies. One particularly useful feature is its SYSINFO system macro variable by which numerical codes are issued following the comparison of data sets. These codes represent up to 16 messages that describe the outcome of the comparisons. Thus, interpreting SYSINFO codes can provide a very concise PROC COMPARE report, especially when many pairs of data sets are involved. In this paper, we demonstrate a novel approach for decoding SYSINFO values using the SAS function bAND (bitwise logical AND). With this function, one can quickly determine which of the 16 PROC COMPARE output messages a SYSINFO code contains |
proc compare, sysrc, proc step, data cleaning, by group processing, if-then-else, clinical data, clinical trial | source | 9 |
| 98 | Deep Dive into the PIM and DDI Data | Michelle Hopkins | Through a contract funded by CMS, work was done to decrease the number of Medicare beneficiaries who have been prescribed a potentially inappropriate medication (PIM) and/or medications that could result in a drug on drug interaction (DDI). SAS 9 was used to analyze Part D claims data to track progress on improving rates as well as taking a deeper dive into the data to identify trends. From baseline to re-measurement, Minnesota saw a relative improvement rate (RIR) of 29.3% for PIMs and 12.6% for DDIs. The nation saw RIRs of 15.4%, and 10.6%, respectively. Minnesota's biggest decrease was in the drug categories of narcotics. This paper will show the analysis done, the SAS code used, and important findings | proc freq, sas enterprise guide, insurance claims, proc step | source | 18 |
| 99 | Defining the Development Process and Governance of Implementing ADaM within an Organization | Chris Decker | Over five years ago, the CDISC ADaM standard was released as a set of principles and best practices. In reality, the model was not really a 'standard' but a general set of guidelines for implementing analysis data; therefore companies avoiding implementing the 'standard'. With the release of the ADaM Implementation Guide at the end of 2009 and the FDA's renewed commitment to standards, companies have realized they have to jump on the ADaM bandwagon or get left behind. This paper presents a case study describing an ADaM development methodology, ADaM implementation lessons learned, and an overall (and ongoing) standards governance process. This case study uses experiences across projects with various companies to combine lessons learned, best practices, things 'not to do' and potential future state to support the development and use of analysis data standards | controlled terminology, adamig, standard deviation, data standard, metadata management, data science, efficacy summary, raw data, cdisc | source | 11 |
| 100 | Developing a Flexible ETL Process to Let SAS Capture Data Changes Efficiently in a Data Warehouse and Clean Up the Mess | Stephen M. Overton | Data sources integrated into an Enterprise Data Warehouse can and most likely will have different extraction frequencies as well as differing time granularities. One efficient way to load a data warehouse is to detect changes in data sources and only keep new data or data which has changed. This paper presents a flexible change data capture process to extract and load new data during any phase of loading a data warehouse. The process can run dynamically at any time and requires no set schedule. This paper will also demonstrate a data retention process using Base SAS. Both processes are centrally managed and operate independent of each other | raw data, dataset, sample, relational database, macro definition | source | 8 |
| 101 | Developing a Predictive Model for Customer Trip Purpose to Be Integrated into Enterprise Strategy and Analytics | Jonathan Levine | Marriott International needed a single accepted metric of leisu re travel as a component of total business that allowed for nuanced measures and the analysis of the impact of specific marketing campaigns. The Marriott Rewards Customer Knowledge team created a predictive model that estimated the likelihood that a stay w as for business. The results of this model will be used both in enterprise -wide reporting visible at the C -level as well as in targeted marketing campaigns. This paper will address some of the modeling approaches used, as well as some of the challenges faced, including specific issues related to building a model to be incorporated into a production ETL. Software used included SAS/STAT, SAS Enterprise Miner , SAS/ACCESS Interface to Netezza, and SAS Scoring Accelerator for Netezza |
data mining, score code, response variable, proc hpsplit, proc step, sas base, data cleaning, likelihood function, business intelligence, data warehouse, neural network, feature selection, if-then-else, regression analysis, proc sql, nominal variable | source | 16 |
| 102 | Developing Custom Metadata Reports for SAS Data Integration Studio | Michael Kilhullen | SAS Data Integration Studio 4.x includes new functionality that allows you to run reports through an interactive reporting facility. You can also create custom reports by using SAS Data Integration Studio software's Java-based plug-in functionality. The plug-in can be written to generate report content using SAS code, Java code, or both. This is ideal for developing reports that specifically address the needs of data integration specialists such as documenting jobs or data standards defined in metadata. This paper takes you through the steps necessary to add a basic metadata report and discusses patterns for developing metadata report plug-ins | sas base, proc step, sample, output delivery system, data standard | source | 12 |
| 103 | Dickens vs. Hemingway: Text Analysis and Readability Statistics in Base SAS | Jessica R. Hampton | Although SAS provides a specific product for text mining (SAS Text Miner), you may be surprised how much text analy- sis you can readily perform using just Base SAS. The author introduces the topic with some background on widely-used readability statistics and tests in addition to a brief comparison of Hemingway and Dickens. After selecting two appropri- ate readability tests and texts of similar length, she describes data preparation challenges, including how to deal with punctuation, case, common abbreviations, and sentence segmentation. Using a few simple calculated macro variables, she develops a program which can be re-used to calculate readability tests on any sample input text file. Finally, she vali- dates her SAS output using published readability statistics from sources such as Amazon and searchlit.org | sample, txt, sas base, macro variable, data wrangling, macro put, proc freq | source | 8 |
| 104 | Doing More with the SAS Display Manager: From Editor to ViewTable - Options and Tools You Should Know | Arthur L. Carpenter | If you have used the interactive interface for SAS you have most likely used the Display Manager. As it ships, the Display Manager is very powerful and yet fairly easy to use with a minimal learning curve for the new user. Because it is functional "right out of the box', most users do very little to customize the interface. This is a shame, because the Display Manager contains a great many hidden opportunities to make it even more powerful, even easier to use, and customized for your way of using the interface. The Display Manager builds a variety of windows, screens, and dialogue boxes to facilitate communication between SAS, the Operating System, and the user. For each of the five primary windows and extending to the dozens of secondary windows there are options that control the content, display, and level of interaction. Options have defaults and a majority of these can be easily changed to suite your needs | proc step, dataset, macro call, proc print, macro facility, macro variable, macro put, macro quoting function | source | 17 |
| 105 | Dynamically Evolving Systems: Cluster Analysis Using Time | David J. Corliss | Cluster analysis, often referred to as Segmentation in business contexts, is used to identify and describe subgroups of individuals with common characteristics that distinguish them from the rest the population. While segments are often identified using st atic characteristics, evolving systems may be better described by how things change over time. A medical patient may be classified by the amount of time since an important event such as a diagnosis, economic activity may be segmented by stages in an econom ic cycle and neighborhoods grouped using by stages in generational evolution. An important success of this technique is in astrostatistics, where a supernova is classified by how the amount of light it produces changes over time. Examples are given in bios tatistics, meteorology and econometrics as well as astrostatistics to demonstrate how time intervals may be used to identify population subgroups in segmentation and cluster analysis | time series analysis, cluster analysis, proc fastclus, proc step, proc standard | source | 9 |
| 106 | Easily Add Significance Testing to your Market Basket Analysis in SAS Enterprise Miner | Michael Faron, Goutam Chakraborty |
Market Basket Analysis is a popular data mining tool that can be used to search t hrough data to find patterns of co-occurrence among objects. It is an algorithmic process that generates business rules and several metrics for each business rule such as support, confidence and lift that help researchers identify "interesting" patterns. Although useful, these popular metrics do not incorporate traditional significance testing. This paper describes how to easily add a well -known statistical significance test, the Pearson's Chi Squared statistic, to the existing output generated by SAS Enterprise Miner's Association Node. The addition of this significance test enhances the ability of data analysts to make better decisions about which business rules are likely to be more useful | market basket analysis, data mining, association rules, data validation rules, chi-square test, likelihood function, hypothesis testing | source | 6 |
| 107 | Effect Modification, Confounding,Hazard Ratio, Distribution Analysis, and Probability of Non-normal Data for Head Neck Cancer | Manojkumar B Agravat | Interaction methods for effect modification and confounding with the O and Oc statistics that are asymptotic chi-square and a PROC IML algorithm with PROC MIXED Agravat (2011) combined with survival and probability analysis for head neck cancer are demonstrated. In support of these new in- teraction analysis methods are C stat, and power. A new hazard logit for survival statistics for head neck cancer due to nonsmoking by race including distribution analysis, hazard ratios, and calculations of probability are shown. Statistics based on probability, independence, and algorithms are important when data are non-normal, linearity is not present, homogeneity assumption for standard error is not met, and when no time point are given. New methods for Z scores and risks based on logits are introduced. Keywords: Asymptotic Chi-square, logit, effect modification,confounding, non-normal, hazard ratio |
hazard ratio, proc step, proc iml, proc mixed, hazard function, fixed effects, p-value, relative risk, binomial distribution, proc calis, cumulative frequency, odds ratio, confidence interval, probability distribution, correlation, sample, proc phreg, regression analysis, data transformation, roc curve, sample size, logistic regression | source | 40 |
| 108 | Enhanced Data Analysis Using SAS ODS Graphics and Statistical Graphics | Patricia A. Berglund | This paper presents practical examples of enhanced data analysis through use of ODS Graphics and the Statistical Graphics (SG) procedures. SAS 9.3 ODS Graphics options and selected SG procedures are demonstrated with a variety of analytic techniques including examination of variable distributions and common regression methods. Procedures such as PROC MEANS, PROC SURVEYMEANS, PROC UNIVARIATE, PROC REG, PROC LOGISTIC, and PROC SURVEYLOGISTIC are used with accompanying ODS Graphics and SG tools for enhanced analysis. The analysis applications include unweighted and weighted analyses and, where appropriate, SAS SURVEY procedures for analysis of data derived from a complex sample design. The techniques presented here can be used in any operating system, and are intended for an intermediate level audience |
proc step, sample, proc sgplot, proc calis, confidence interval, histogram, regression analysis, logistic distribution, logistic regression, proc surveylogistic, proc surveyreg, random sampling, proc freq, interval variable, odds ratio, proc logistic, proc univariate, data science, degrees of freedom, proc surveymeans | source | 20 |
| 109 | Error Reduction and Report Automation Approaches for Textually Dense Pharmaceutical Regulatory Conformance Incident Data | Barry deVille, Mark Wolff |
The Periodic Safety Update Report (PSUR)1 is a significant feature of the post-trial, in-market monitoring of drug efficacy. The manufacturer's response to a drug-use incident is comprehensive: new literature must be searched, a wide range of interactions with related agents must be examined, and finally, a complete clearance must be claimed. This response is a huge information-processing burden due to the many factors at play and because so much of the information - starting with the original exception report (including numerical data) - is delivered in unstructured textual format. This presentation discusses a proof of concept carried out on behalf of a major pharmaceutical manufacturer that deployed a variety of text content identification and manipulation techniques to eliminate manual error and automate the report production | sas enterprise guide, adverse events | source | 14 |
| 110 | Essential SAS ODS PDF | Patrick Thornton | This paper lists and demonstrates the ODS techniques that I have found most useful in creating user friendly PDF reports in SAS 9.2 on a Windows operating system. Basic ODS techniques include creating PDF files (e.g. ODS PDF FILE= STYLE= KEYWORD= COLUMNS=), modifying page setup (e.g. orientation, date, title, and footnote), and saving procedural output to data sets (e.g. ODS OUTPUT), The examples also demonstrate simultaneously creating two PDF destinations (i.e. ODS PDF ID=) and selectively including or excluding procedural output to each destination (e.g. ODS PDF SELECT, EXCLUDE and WHERE=). Two PDF files are simultaneously created where PROC FREQ listings are directed to one file and selected PROC FREQ results are combined with PROC REPORT to create an executive summary in the other PDF file |
proc step, ods pdf, proc freq, proc report, macro variable, proc sql | source | 10 |
| 111 | Ethnicity and Race: When Your Output Isn't What You Expected | Philamer M. Atienza | In SAS, when a classification variable is used to group observations with the same values and a formatted value is used for grouping data, unexpected results may come out of the procedure. If there is more than one unformatted value used for several distinct categorizations but with the same format label, SAS uses the unformatted lowest value to create the output. Understanding the behavior of SAS when storing the unformatted values will help avoid potential mistakes in using formats and nested classification variables. This paper examines two scenarios when a variable for both ethnicity and race is used in PROC TABULATE to create an output data set: (1) with, and (2) without the use of a format |
proc tabulate, proc step, sample, proc freq | source | 5 |
| 112 | Evolution of Best Practices for Metadata Change Control in SAS 9 | Diane Hatcher | The concepts for change control around the SAS Metadata Server have remained fairly constant throughout the releases across SAS9. The best practices for how to manage the change process, though, have evolved as the tools have evolved. This paper identifies some best practices for implementing processes using the metadata promotion tools, comparing SAS 9.1.3, SAS 9.2, and SAS 9.3. The paper is intended for SAS system administrators who are familiar with the SAS architecture and the role that the metadata server plays. We focus on how to structure the metadata environment, the tools available for handling changes in metadata, and best practice processes that control how change occurs | sas management console, spk file, data cleaning | source | 13 |
| 113 | Evolving from Data Management to Master Data Management | Wilbram Hazejager | The proliferation of enterprise-level applications (along with expectation for shared, synchronized information) drives the need for the development of a single view of the key data entities in common use across the organization. Therefore, master data management (MDM) has become one of the fastest-growing application areas in recent times. This presentation provides an overview of how DataFlux qMDM can enhance your data management initiatives and put you on the path to developing a complete view of your data | data governance, master data management, entity resolution, data cleaning, data integration | source | 8 |
| 114 | Examples of Building Traceability in CDISC ADaM Datasets for FDA Submission | Xiangchen (Bob) Cui, Hongyu Liu, Tathabbai Pakalapati |
Traceability in context of ADaM data sets means providing the method followed to derive an analysis endpoint from source SDTM data. CDISC ADaM IG 1.0 strongly recommends the incorporation of traceability feature in ADaM data sets submitted to FDA. Traceability in derived data sets increases confidence and provides transparency to agency reviewers which might help in expediting the review and approval process. This paper provides examples in applying the inherent traceability features available in ADaM Basic Data Structures (BDS), adding SRCDOM, SRCVAR, and SRCSEQ variables and with examples about adding Relation Criteria and Relation Factor variables in ADaM data sets [2]. This paper tries to provide insight on tradeoffs and limitations of traceability. The examples in this paper were from FDA submissions | adam dataset, sdtm domain, sample, adamig, concomitant medications, cdisc | source | 14 |
| 115 | Excel Tool for Coding | Ronald Palanca | Open-ended coding is the method of assigning numeric values to verbatim questionnaire responses for the purpose of categorizing the responses for analysis. To perform this task, coders go through the list of verbatim questionnaire responses, and then refer to another list for the assigned numeric values to determine which value to assign. This paper presents a cost-effective way to set up a front end for the coders to do the coding by using a SAS dataset. An EXCEL spreadsheet with the actual question, the verbatim questionnaire responses, and the lookup codes is created in the same worksheet, and each sheet is categorized by question number. There is no need for a predefined template |
dataset, proc step, excel | source | 4 |
| 116 | Excelling with Excel | Tim Beese, Greg Granger |
Microsoft EXCEL is the most widely used tool for data analysis, and it is the default entry point for many consumers wanting to explore and analyze data. While EXCEL is suitable for basic analysis, it does not provide the powerful analytics available in SAS. With the SAS Add-In for Microsoft Office, users of EXCEL can seamlessly leverage the power of SAS analytics while providing secure access to data and IT resources previously unavailable through EXCEL. In this paper, the use of SAS Add-In for Microsoft Office running under Microsoft EXCEL is demonstrated. This paper also discusses the use of cell values and ranges in EXCEL worksheets as input and output to SAS Stored Processes |
excel, proc step | source | 28 |
| 117 | Executing a PROC from a DATA Step | Jason Secosky | All programs are built by creatively piecing together simpler parts into a larger whole. In SAS, the SAS macro facility provides an ability to group and piece together analytic blocks. However, writing complex programs using the SAS macro facility can be difficult and cumbersome. An easier way is to combine two new functions, RUN_MACRO and DOSUBL, with DATA STEP code. RUN_MACRO and DOSUBL enable DATA STEP code to immediately execute a macro and act on the result, something that was not possible before SAS 9.2. This paper presents examples of using these two new functions from DATA STEP to code programs that are easier to read, write, and maintain |
proc step, macro variable, proc sql, proc http, web service, sas base, proc fcmp, macro facility, macro definition | source | 12 |
| 118 | Exploratory Factor Analysis with the World Values Survey | Diana D. Suhr | Exploratory factor analysis (EFA) investigates the possible underlying factor structure (dimensions) of a set of interrelated variables without imposing a preconceived structure on the outcome (Child, 1990). The World Values Survey (WVS) measures changes in what people want out of life and what they believe. WVS helps a worldwide network of social scientists study changing values and their impact on social and political life. This presentation will explore dimensions of selected WVS items using exploratory factor analysis techniques with SAS PROC FACTOR. EFA guidelines and SAS code will be illustrated as well as a discussion of results |
factor analysis, factor loading, correlation, proc step, proc factor, sample, latent variable, r-squared, proc corr, sample size, maximum likelihood estimation, standard deviation, likelihood function, scree plot, estimate, varimax rotation | source | 19 |
| 119 | Exploring DATA Step Merges and PROC SQL Joins | Kirk Paul Lafler | Explore the various DATA STEP merge and PROC SQL join processes. This presentation examines the similarities and differences between each, and provides examples of effective coding techniques. Attendees examine the objectives and principles behind merging and joining, as well as the coding constructs associated with inner and outer merges and joins, and hash techniques |
hash table, proc sql, proc step, lookup, inner join, if-then-else, create table, right join | source | 14 |
| 120 | Exploring the Dimensionality of Large-Scale Standardized Educational Assessments Using PROC FACTOR | Jonathan Steinberg | Standardized educational assessments test students in specific subject areas or measure certain core competencies. Educational researchers regularly use exploratory factor analysis (EFA) to understand a test's internal structure related to its design. PROC PRINCOMP may be used; yet, it has limitations in dealing with the potentially complex structure of standardized test data. This paper will demonstrate how PROC FACTOR is more useful in two ways. First, chi-square hypothesis tests can determine whether a specified number of factors fit the data, particularly when no a priori hypotheses exist about the test's internal structure. Secondly, rotation of multiple factors can be employed to account for inherent inter-factor correlations. This paper is intended for those with good knowledge of multivariate statistics and moderate levels of SAS programming experience |
proc step, proc factor, correlation, factor analysis, factor loading, proc princomp, covariance, scree plot, sample, maximum likelihood estimation, likelihood function, chi-square test, proc corr | source | 10 |
| 121 | Factoring Upgrades into Overbooking Decisions for Hotels and Casinos | Alex Dietz, Natalie G. Osborn, Tugrul Sanli |
Overbooking is a must for any business that accepts reservations and subsequently runs the risk of cancellations and no-shows. In hotels, the practice of overbooking requires a fine balance between guest service, operational procedure, and revenue optimization. For hotels with multiple room types, the complexity is multiplied. Adding new room types allows you to reach a broader market, but every new room type added increases the risk of underutilized rooms. An overbooking strategy that properly accounts for upgrades helps you make better decisions on pricing, inventory control, and overbooking. This paper explores a methodology and the benefits from factoring upgrades into overbooking decisions for hotels and casinos | lapse rate, deterministic model | source | 7 |
| 122 | Faking Esri ArcGIS Maps in SAS | Anastasiya M. Osborne | This paper describes another successful project of automating manual tasks at the Farm Service Agency, USDA. After an urgent request, new SAS code with PROC GPROJECT, PROC GMAP, and heavy use of annotation was developed to map widely used state revenue guarantees and benchmark yields for 22 crops under the Congressionally-mandated Average Crop Revenue Election (ACRE) program. A program is administered by the Farm Service Agency, USDA. Previously, such maps were created in ArcGIS. The SAS solution enhanced the quality of the map: it showed state revenues and yields in a compact but readable way, drastically improved presentation of data in Northeast states, and eliminated repetitive task of making a PDF file out of 22 separate maps |
excel, macro variable | source | 18 |
| 123 | Finding the Winning Combination: An Application of Multivariate Testing from Digital Marketing | Robert Krutsick | When designing a marketing campaign, marketers must identify the appropriate mix of offer elements to optimize their desired measure of campaign performance (such as response, conversion, sales, and profit). This is not usually a trivial task since the number of offer elements making up a typical marketing campaign can be quite large. The chosen mix of offer elements included in a campaign is usually based on a combination of business judgment and the results of experimental testing. In this paper, we present an application of multivariate testing from the field of digital marketing analytics. We demonstrate how to design a fractional factorial test with JMP and how to predict the optimal combination of offer elements with the GLIMMIX procedure |
main effect, proc glimmix, proc step, estimate, response variable | source | 8 |
| 124 | Finding Your Inner Query with SAS Enterprise Guide | Michael R. Burke, I-Kong B. Fu |
SAS Enterprise Guide 5.1 introduces the concept of reusable, query-based templates. SAS Enterprise Guide users have been able to create task templates to save custom settings for some time now. Query-based templates offer the ability to create similar functionality from the SAS Enterprise Guide Query Builder. These templates allow speedy access to previously designed queries that might have included time-consuming work spent fashioning the perfect table join, computed column, or filter. SAS Enterprise Guide 5.1 also allows you to reuse certain types of query-based templates as subqueries in computed columns and filters. This paper describes how query-based templates work and how to use them to answer special problems by turning them into subqueries in the point-and-click Query Builder interface | sas enterprise guide, proc sql, create table, proc step, where statement, right join | source | 40 |
| 125 | Finding Your Way Around a SAS Generated Report | William C. Murphy | Using the procedures of the SAS system, we can and do generate hundreds of tables. The Output Delivery System easily renders these tables in rich text format. Navigating around the resulting report would be quite daunting if not for the Microsoft Word Table-of-Contents tool. Before you can use this tool profitably, careful attention must be paid to the Word settings and the SAS program that creates the tables. Appropriate use of the CONTENTS option and the ODS PROCLABEL statement must be made to achieve a useful result. Using these options along with PROC REPORT, we will demonstrate how to generate a clear and definitive table-of -contents in your report. This method will then be extended to other procedures, like PROC PRINT and PROC TABULATE |
proc step, proc report, proc tabulate, proc print, output delivery system | source | 5 |
| 126 | Formation of Optimum Accountable Care Organizations (ACOs) Leveraging SAS/OR | Khasha Dehnad | Under the new health care reform law a network of doctors and hospitals, can form an "Accountable Care Organization (ACO)" and be awarded the responsibilities of managing and delivering all of health care needs of Medicare beneficiaries. In this paper, we show how the rich language of PROC OPTMODEL and Patient-Level data can be used to facilitate the formation of optimum ACOs. Our methodology relies on identifying the optimum combination of social/referral networks of general practitioners, specialists and hospitals while meeting Medicare contractual requirements. Representing practitioners as nodes and "common/referred patients between practitioners" as links, "PROC OPTMODEL" is utilized to calculate the shortest path between all nodes. Then links with the highest number of traversed shortest paths (betweeness) are iteratively removed in order to identify distinct sub communities of practitioners |
odds ratio, proc step | source | 8 |
| 127 | From Solo to Symphony: Scale Service Up Using Multiple Stored-Process Server Clusters | Qing Ye, Richard Nardin, Romon Williams |
System architects today often face a difficult challenge: how to scale up existing services to serve more clients, who not only have more requests but also demand faster responses. The answer is to create a server cluster with the support of load balancing. Clusters created using SAS Stored Process Server provide a way to define a service in a metadata server, balance its load for various clients by using Object Spawners, and execute the implementation across different hosts to achieve the desired performance. This paper shows how to set up a server cluster by connecting multiple stored process servers and how to configure the load balancer | sas management console, sas grid | source | 12 |
| 128 | Generation Why: How Generation Data Sets Can Help | Lisa Eckler | Generation data sets are powerful tools available in SAS to manage versions of data. This paper will address what generation data sets are, how they are defined and referred to, and why you should use them to improve programming productivity and achieve simple and elegant management of historical versions of data in SAS | macro variable, proc step | source | 7 |
| 129 | Get Your "Fast Pass" to Building Business Intelligence with SAS and Google Analytics | Patricia S. Aanderud, Angela M. Hall |
See the magic of Google Analytics with SAS Business Intelligence tools! Use your "Fast Pass" to learn SAS Information Map Studio, SAS Web Report Studio, and SAS Information Delivery Portal with the authors of the newly released "Building Business Intelligence with SAS". We will guide you step-by-step through creation of information maps, custom web reports, and portal pages using data from Google Analytics. You will learn some tips and tricks for creating custom data items, designing and linking reports, and adding external content to your portal | business intelligence, dataset, excel | source | 18 |
| 130 | Getting Started with ODS: Generating Formatted Reports Using the ExcelXP Tagset | Allan Del Rosario, Jennifer Mefford |
In the business world, reporting is a key component in presenting data to the user. Using the power of the SAS ODS ExcelXP tagset destination to customize report structure and format is one method of report creation. This method is particularly well-suited to automating the production of multiple EXCEL workbooks that are nicely laid out for users. This paper shows a simple approach to getting started with writing ODS using the ExcelXP tagset. It demonstrates how to use the ODS style options to generate formatted reports in EXCEL directly from SAS, and demonstrates how to easily add iterative DO loops and macro variables to automatically generate multiple formatted reports |
excel, macro variable, proc step, proc print, proc template, do loop | source | 8 |
| 131 | Getting to the Good Part of Data Analysis: Data Access, Manipulation, and Customization Using JMP | Audrey Ventura | Effective data analysis requires easy access to your data no matter what format it comes in. JMP can handle a wide variety of formats. Once the data is in JMP, you can choose from a variety of options to reshape the data with just a few clicks. Finally, customize your data with labels, colors, and data roles so that graphs and charts automatically look the way you want them to. This paper walks through two or three story lines that demonstrate how JMP can easily import, reshape, and customize data (even large datasets) in ways that allow your data to be displayed in vibrant visualizations that will wow your audience | dataset, data science, line chart, excel | source | 19 |
| 132 | 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 |
| 133 | Global Risk Management: How SAS Manages Financial Risk According to Diverse Financial Regulations | Ting Xu, Hao Qiu, Shibin Liu |
Banks need to measure risks according to local regulations, such as Basel II/III, Capital Requirements Directive, etc. Multinational Banks are facing the challenge of global compliance of diverse risk regulations. SAS provides various solutions to help banks solve this challenge. This paper depicts a simulated scenario: Analysis environment is set up with instruments, portfolio data and market data in different language for different subsidiary; a set of methods and analyses is defined according to requirements of European Central Bank, Federal Reserve Board, and China Banking Regulatory Commission respectively.; After the project is run, capital is calculated for subsidiaries with corresponding regulation and local market data; stress testing is then applied. From the demo, you will learn that SAS Risk Management solutions perfectly supports the measurement of quantitative risk based on multiple regulations and can be implemented for Multinational banks | sas risk, stress testing, credit risk, transaction data | source | 14 |
| 134 | Go Beyond the Wizard with Data-Driven Programming | Renato G. Villacorte | Programming techniques that take advantage of Data-Driven Programming will be demonstrated for Novice and Intermediate Users of Base SAS. Most users already take advantage of Data-Driven Programming with wizards. Wizards harvest information through an interface and then write and execute programs based on those parameters. As programmers' skills evolve, they may want to edit the product of a wizard in order to confront a variety of problems that a wizard could not anticipate. Going a step further, programmers can author their own wizards that make their own work easier. The workshop will include demonstrations in working with built-in SAS wizards, developing simple Data-Driven Programs, and the use of parameter-gathering techniques using Enhanced Editor, simple macro language, and ODS |
proc step, proc import, macro variable, output delivery system, logistic distribution, proc contents, macro definition | source | 7 |
| 135 | Good Programming Practices: Creating Robust Programs | Gregory S. Barnes Nelson, Jay Zhou |
proc step, error message, sas programming, sas log, missing value, if-then-else, merge statement, proc sql, user-defined format, work library | source | 16 | |
| 136 | Graphing Made Easy with SG Procedures | Lora D. Delwiche, Susan J. Slaughter |
New with SAS 9.2, ODS Graphics introduces a whole new way of generating graphs using SAS. W ith 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 SGSCATTER. 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, scatter plot, histogram, proc sgpanel, density plot, phase ii | source | 19 |
| 137 | Guidelines for Preparing Your Computer Systems for SAS | Margaret A. Crevar, Tony Brown |
Have you ever wondered if you are really prepared to start the installation process of SAS software on your hardware? Perhaps you have read the System Requirements sheets for your SAS release and version, and the appropriate SAS platform administration guide for your operating system. Are there other crucial items to consider before the installation process that are targeted toward your company's expected performance of SAS? | dataset, data science | source | 10 |
| 138 | Handling Missing Data by Maximum Likelihood | Paul D. Allison | Multiple imputation is rapidly becoming a popular method for handling missing data, especially with easy-to-use software like PROC MI. In this paper, however, I argue that maximum likelihood is usually better than multiple imputation for several important reasons. I then demonstrate how maximum likelihood for missing data can readily be implemented with the following SAS procedures: MI, MIXED, GLIMMIX, CALIS and QLIM |
missing value, imputation, likelihood function, proc step, maximum likelihood estimation, regression analysis, multiple imputation, response variable, proc calis, missing at random, proc mi, proc reg, estimate, covariance, sample, posterior probability, proc mixed, missing completely at random, correlation, normal distribution, logistic distribution, main effect, predictor variable, random intercept, logistic regression, proc glimmix, prior probability, standard deviation, nominal variable, binary variable, latent variable, longitudinal data, numeric variable, sample size, binomial distribution, poisson distribution | source | 21 |
| 139 | Have Your Web Reports Remember the Filters of Your Users | Frank Poppe, Andres Van Antwerpen |
SAS Web Report Studio offers several ways of filtering your data. However, often users would like to define a filter only once, and have this filter applied automatically when opening different web reports. This paper describes a way of accomplishing just that: the user makes a selection in SAS Information Delivery Portal, which then is stored and applied automatically to an information map using the SAS Stored Process Server. In this way, each web report that uses this information map will automatically show source data filtered for the user's selection | macro variable, work library, proc sql | source | 6 |
| 140 | How Does SAS In-Database Analytics Impact Data Management? | Adrian Jones | Over the last few years, business analysts have been able to realize the benefits of SAS In-Database Analytics through both performance improvements and value generation. With SAS In-Database, users interact with the data changes along with the process to develop and deploy modeling outputs. This not only has an impact on the business processes to support analytical activity, but also on the underpinning storage and data management processes. This paper takes a look back at the uptake of SAS In-Database Analytics and the impact it has had on the data architecture and data management processes required to support such activity | data governance, sas model manager, analytics platform, data integration, data standard, in-database processing, model deployment | source | 8 |
| 141 | How Readable and Comprehensible Is a SAS Program? A Programmatic Approach to Getting an Insight into a Program | Rajesh Lal, Raghavender Ranga |
In any programming language, there are general guidelines for writing "better" programs. Well-written programs are easy to re-use, modify, and comprehend. SAS programmers commonly have guidelines for indentation, program headers, comments, dead code avoidance, efficient coding, etc. It would be great if we could gauge a SAS program's quality by quantifying various factors and generalize for individual programming styles. This paper aims to quantify various qualitative characteristics of a SAS program using Perl regular expressions, and provide insight into a SAS program. This utility, when used across a large project, can serve as a tool to gauge the quality of programs and help the project lead take any corrective measures to ensure that SAS programs are well written, easily comprehensible, well documented and efficient | proc step, flag variable | source | 12 |
| 142 | How Test Length and Sample Size Have an Impact on the Standard Errors for IRT True Score Equating: Integrating SAS and Other Software | Yi-Fang Wu | The standard error of equating is a useful index to quantify the amount of equating error. It is the standard deviation of equated scores over replications of an equating procedure in samples from a population or populations of examines. The current study estimates the SE of item response theory true score equating in the Nonequivalent Groups with Anchor Test design using simulations. Specifically, the test length of the internal anchor and the sample size are of interests. Some specialized programs, such a s BILOG -MG 3.0 for item calibration, ST for IRT scale transformations, and PIE for IRT true score equating, are incorporated to accomplish the equatings. The purpose of the paper is to demonstrate such a complicated and repetitive procedure through SAS | sample, sample size, proc calis, estimate, standard deviation, macro variable | source | 12 |
| 143 | How to Administer a 9,000-User Community on a Multiple SAS Metadata Environment | Frank Baars, Edwin Nijsen |
Growing from a relative simple SAS 9.1.3 BI, SAS Data Integration Studio environment with 5 metadata servers and up to 600 users to a complex structure of in total 15 metadata servers and potentially 9,000 users, our user administration activities needed a serious update. As user administration was still done by hand, a new automated approach on user administration was essential. This paper proposes an automated user administration solution including single sign-on facilities and role-based access for a multiple SAS platform environment | business intelligence, access control | source | 8 |
| 144 | How to Create a Business Intelligence Strategy | Guy R. Garrett | You may wonder how some organizations are so astute at managing to keep abreast of changing customer behavior in the market. There are undoubtedly many factors - however central to the answer is that they recognize the value of their information assets and alter their strategic vision with new perspective. A Business Intelligence Strategy is a roadmap that enables businesses to measure their performance and seek out competitive advantages and truly "listen to their customers" usi ng data mining and statistics | business intelligence, data cleaning | source | 8 |
| 145 | HOW to DOW | Paul M. Dorfman, Lessia S. Shajenko |
The DOW-loop is a nested, repetitive DATA STEP structure enabling you to isolate instructions related to a certain break event before, after, and during a DO loop cycle in a naturally logical manner. Readily recognizable in its most ubiquitous form by the DO UNTIL(LAST.ID) construct, which readily lends itself to control break processing of BY group data, the DOW loop's nature is more morphologically diverse and generic. In this workshop, the DOW-loop's logic is examined via the power of example to reveal its aesthetic beauty and pragmatic utility. In some industries like Pharma, where flagging BY group observations based on in-group conditions is standard fare, the DOW-loop is an ideal vehicle, greatly simplifying the alignment of business logic and SAS code. In this hands-on workshop, the attendees will have an opportunity to investigate the program control of the DOW-loop step by step using the SAS DATA STEP debugger |
set statement, hash table, missing value, output, by group processing, dataset, automatic variable, proc sql | source | 12 |
| 146 | How to Dynamically Conditionalize a SAS Data Integration Studio Job | Riku E. Jokinen | Data driven conditional processes in SAS has always been possible with macros. With SAS Data Integration Studio, you might find macro based solutions either unwanted due to most of the code not being visualized in the job, or impossible due to metadata not being able to follow the actual data. The Loop transformation can be used to dynamically conditionalize SAS code without using user written macros. This enables data driven DI Studio jobs that will perform certain parts of the code only if defined conditions apply. It also allows separate parts of the code to be run either iteratively or conditionally while maintaining metadata intact throughout the job | sas base, sas data integration studio | source | 4 |
| 147 | How to Measure the SAS BI Audience and Discover Information Needs | Plinio Faria | It's very useful to measure an audience using SAS Web Report Studio. It helps you create better reports and know the users' information needs, and it will guide the development of your reporting system. Many times, there is too much information available, and users feel that it is too difficult to get what they really need. It is important to create comprehensive reports and to keep only those that are being regularly viewed. This presentation shows how to measure how often a report is accessed using SAS Web Report Studio log files, and how to combine this data with user data to classify users into groups. You can analyze what type of reports should be available and the way users like to see these reports | proc sql, proc step | source | 6 |
| 148 | How to Perform and Interpret Chi-Square and T-Tests | Jennifer L. Waller | For both statisticians and non-statisticians, knowing what data look like before more rigorous analyses is key to understanding what analyses can and should be performed. After all data have been cleaned up, descriptive statistics have been calculated and before more rigorous statistical analysis begins, it is a good idea to perform some basic inferential statistical tests such as chi-square and t-tests. This workshop concentrates on how to perform and interpret basic chi-square, and one- and two-sample t-tests. Additionally, how to plot your data using some of the statistical graphics options in SAS 9.2 will be introduced | proc step, proc means, interval variable, nominal variable, sample, box plot, chi-square test, standard deviation, proc freq, data science, proc ttest, proc univariate, ordinal variable, variable list, inferential statistics, heteroscedasticity, confidence interval, homoscedasticity, missing value, one-sample t-test, histogram, proc sgplot, class variable, frequency analysis, sample size, outlier | source | 20 |
| 149 | How to Use SAS/CONNECT to Get Your Mainframe to Behave Like a Modern Computer in Your New Business Intelligence Platform | John C. Hennessey | This paper reports on the ability to embed SAS/CONNECT code in a stored process on a SAS Business Intelligence (BI) platform. The Social Security Administration's Office of Research, Evaluation, and Statistics (ORES) has a BI platform installed on a Windows server to produce summary statistics and data extracts for the analysis of Social Security programs. The BI platform must communicate with large mainframes to access and process the operational data of the Social Security Administration. Since 2006, we have been developing ways to use SAS Enterprise Guide and SAS/CONNECT to bring the mainframe into a modern interface. We are currently developing stored processes that can be used by our internal customers to run directly without ORES intervention |
business intelligence, sas enterprise guide | source | 4 |
| 150 | Hyperslicing with SAS | Jonathon Khoo, Ping Shung Koo |
Hyperslice is one of the new er techniques in visualization that involves the viewing of high dimensional data in a simple visualization . The data is centered around a chosen point based on a certain dimension, creating a slice . The user can further explore this data through manipulating this chosen point. However, as SAS does not provide this type of interactivity directly, in this paper, we explore some possible ways to implement this extreme slicing of data and exploration of the information using the same concepts proposed in Hyperslice within different aspects of S AS | proc sgscatter, proc step, sas base, regression analysis | source | 10 |
| 151 | Implementing Control Selection Using Hash Tables: A Case Study | Craig Kasper | This paper discusses a program written to perform matched control selection from a research population, using hash tables to eliminate the need for a large temporary data set, and allowing the process to run significantly faster as a result. It describes the development of the program from the point of initial need to the completion of the working program, and discusses how insights into the control selection process enabled the transformation of the program from using conventional methods to one using dynamic programming | hash table, proc surveyselect, proc step, data step, set statement, pdv, likelihood function, macro loop | source | 10 |
| 152 | Import and Output XML Files with SAS | Yi Zhao | XML files are widely used in transporting data from different operating systems or data storages. Processing XML files efficiently becomes one of the necessary technical skills for SAS programmers. This paper presents four aspects in importing and exporting XML files with SAS. They include: (1). Using XML engine LIBNAME and DATA STEP to import or output data between XML and SAS datasets. (2). Using PROC COPY. (3). Developing XMLMap to facilitate the conversion; and (4). Importing XML file to EXCEL and converting it to SAS dataset. Sample program codes and tips are provided and discussed |
dataset, excel, proc step, proc copy, proc print, sas programmer, proc import | source | 6 |
| 153 | Improving Retail Decisions with Customer Analytics: Leveraging Actionable Customer Insights to Build Sales and Profits | Wanda Shive, Dwight J. Mouton |
Customer-centric decision making is a key differentiator for retailers. Collecting customer data is a common practice. Major challenges include making customer offers more personal and delivering actionable customer insights to merchants at the point of decision for assortment, pricing, and promotions. This paper highlights how retailers are increasing sales and profitability by using optimization to improve redemption rates of customer-specific offers and by adding the customer dimension to key merchandising decisions | subscriber analytics, data validation rules, customer segmentation, service provider | source | 7 |
| 154 | Including the Salesperson Effect in Purchasing Behavior Models Using PROC GLIMMIX | Philippe Baecke, Dirk Van den Poel |
Nowadays, an increasing number of information techn ology tools are implemented in order to support dec ision making about marketing strategies and improve custo mer relationship management (CRM). Consequently, an improvement in CRM can be obtained by enhancing the databases on which these information technology to ols are based. This study shows that a salesperson's person al attitudinal and behavioral characteristics can h ave an important impact on his sales performance. This salesperson effect can be easily included by means of a generalized linear mixed model using PROC GLIMMIX. This can sig nificantly improve the predictive performance of a purchasing behavior model of a home vending company |
logistic distribution, logistic regression, proc glimmix, proc mixed, sample, regression analysis, predictor variable, proc step, correlation, estimate, response variable, data cleaning, likelihood function, proc freq, backward selection, binary variable, binomial distribution, proc genmod | source | 11 |
| 155 | Increase your OUTPUT with PROC MEANS and PROC FREQ | Julie Kezik, Melissa Hill |
How many people have "x and y'? How many people are in category "z'? The questions are endless, but the solutions are not! Using an output statement in PROC MEANS or PROC FREQ can easily answer those inquiries. The purpose of this paper is to utilize the OUTPUT statement in a PROC MEANS or PROC FREQ to get simple statistics for a selected variable or group of variables. The resulting SAS dataset can be used for future analyses and/or be easily exported to create graphical or tabular displays. Problem With large datasets it is often hard to tease out necessary simple statistics that analysts are required to produce on a regular basis. Using PROC MEANS and PROC FREQ can be solutions to frequently asked questions and reporting queries. The Question We are asked to find simple statistics on the age distribution of children with allergies, stratified by gender. Let's first take a look at the variables, we can utilize the means and frequency procedures to do the investigating |
proc step, proc freq | source | 7 |
| 156 | Inflated Beta Regression: Zero, One, and Everything in Between | Christopher J. Swearingen, Maria S. Melguizo, Zoran Bursac |
Beta Regression, an extension of generalized linear models, can estimate the effect of explanatory variables on data falling within the (0,1) interval. Recent developments in Beta Regression theory extend the support interval to now include 0 and 1. The %Beta_Regression macro is updated to now allow for Zero-One Inflated Beta Regression. KEY WORDS: Beta Regression, Inflated Beta Regression, PROC NLMIXED, Macro, Generalized Linear Model |
regression analysis, response variable, proc calis, estimate, beta distribution, predictor variable, proc step, clinical trial, macro call, macro definition, likelihood function | source | 11 |
| 157 | Innovative Uses of ODS and GTL | Kathy Chen | Graphical representation of treatment effect is often easier to understand and is therefore frequently used in conjunction with summary reports. The Output Delivery System (ODS) Graphics Template Language (GTL) when used with a DATA STEP or PROC SGRENDER procedure allows you to design your own layout so you can easily incorporate statistical graphics into summary reports. ODS Graphics have facilitated the automatic creation of statistical graphics and that makes SAS a more viable tool for rapid production of graphics. This paper will describe how to use ODS Graphics and GTL to effectively report study results with dynamic visualization of data. Topics include: Customizing summary reports by incorporating graphics using GTL Making graphics less restricted to input data by using GTL EVAL function Creating statistical graphs from SAS procedures with ODS graphs Editing graphs |
proc step, proc template, proc sgplot, macro variable, treatment effect | source | 12 |
| 158 | Integrating Enterprise Search with Various Clients | Arvind Jagtap | In today's world of information technology, the information is exploding through the Internet. Search has become an integral part of life. Enterprise contents are no exception and the enterprise search plays a major role in finding the required information quickly to enhance the productivity of business executives. A need for searching can arise at any time while working with some popular applications. Hence, integrating search features with these applications can be helpful. As a part of SaaS, SAS provides "Search Interface to SAS Content" as a service for searching enterprise contents. This paper demonstrates how this service can be invoked from various clients like Microsoft Office, Internet Explorer, SharePoint, and Google Search Appliance to allow users to search the required contents without leaving the application | business intelligence, excel | source | 10 |
| 159 | Integrating SAS with ERwin to Capture Design and Business Metadata | Jim Ferrari, Juan P. Olvera |
Do you have difficulty documenting architectural or design ideas? Are naming standards and consistency throughout the data warehouse difficult to maintain? Do related fields have unrelated names in different areas of the data warehouse? A modeling tool like ERwin can assist with some of these issues. But how do you integrate a modeling tool like ERwin with your SAS BI environment? Learn data architect and modeling techniques that will help refine your ideas. Learn how to integrate the metadata from ERwin (or similar tool) into your SAS environment. The metadata can be extracted into tables for reporting purposes (data dictionary, requirements, etc) or imported using a metadata bridge to populate the SAS metadata directly | data dictionary, data warehouse | source | 6 |
| 160 | Integrating Your Java Web Application into the SAS 9.2 or SAS 9.3 Enterprise Business Intelligence Environment | Guillaume Curat | Do you want your existing Java Web application to look and behave like the other SAS applications? The key to achieving this goal is the use of the SAS Logon Manager, SAS metadata roles and capabilities, SAS themes, the SAS Logging Service, and metadata. This paper explains in detail all the steps to integrate your Java Web Application to an existing SAS Enterprise Business Intelligence (BI) environment. SAS AppDev Studio is used to demonstrate how easily it can be done | sas management console, business intelligence | source | 22 |
| 161 | Intelligent PROC SORT NODUPKEY | Andrea Wainwright-Zimmerman | Have you ever had a data set that you were updating records in and needed to eliminate the old records and replace them with new records? The SORT procedure with the NODUPKEY option eliminates rows that duplicate your key fields, choosing which record to keep based on its own logic, which might not be the one you want. This paper shows how to combine a PROC SORT and a DATA STEP to get the sorted data set with the exact records that you want |
proc sort, proc step | source | 5 |
| 162 | Interesting Technical Mini-Bytes of Base SASFrom DATA Steps to Macros | Airaha Chelvakkanthan Manickam | Over the last several decades, SAS has improved and added thousands of features to Base SAS. It is almost impossible for someone to know all of the tricks and tips of Base SAS. This paper highlights some useful tips and advanced tricks of Base SAS that I have encountered during my experience working with SAS. These technical mini-bytes are divided into two sections-DATA STEP mini-bytes and macro mini-bytes. In this paper, I present interesting examples involving common and advanced DATA STEP executions such as CALL SET, dynamically accessing SAS data sets, and PERL pattern matching. Similarly, simple to advanced macro tips including CALL EXECUTE and QUOTE functions in macros are discussed |
data step, call execute, sas base, set statement, macro variable, raw data, proc step, put statement, sample, data validation rules, pharmaceutical industry, reference data, variable list, proc print, by group processing, key=, macro call, macro definition, master data management, proc sql | source | 13 |
| 163 | Introducing the FMM Procedure for Finite Mixture Models | David Kessler, Allen McDowell |
Y ou've collected the data and performed a preliminary analysis with a linear regression. But the residuals have sev- eral modes, and transformations don't help. Y ou need a different approach, and that calls for the FMM procedure. PROC FMM fits finite mixture models, which enable you to describe your data with mixtures of different distributions so you can account for underlying heterogeneity and address overdispersion. PROC FMM offers a wide selection of continuous and discrete distributions, and it provides automated model selection to help you choose the number of components. Bayesian techniques are also available for many analyses. This paper provides an overview of the capabilities of the FMM procedure and illustrates them with applications drawn from a variety of fields |
poisson distribution, likelihood function, proc step, proc reg, estimate, regression analysis, poisson regression, response variable, sample, maximum likelihood estimation, bayesian statistics, binomial distribution, histogram, zero-inflated poisson, normal distribution, normality test, posterior probability, proc univariate, proc calis, proc genmod | source | 22 |
| 164 | Introduction to Predictive Modeling with Examples | David A. Dickey | Predictive modeling is a name given to a collection of mathematical techniques having in common the goal of finding a mathematical relationship between a target, response, or "dependent" variable and various predictor or "independent" variables with the goal in mind of measuring future values of those predictors and inserting them into the mathematical relationship to predict f uture values of the target variable. Because these relationships are never perfect in practice, it is desirable to give some measure of uncertainty for the predictions, typically a prediction interval that has some assigned level of confidence like 95%. Another task in the process is model building | proc princomp, regression analysis, proc step, estimate, correlation, proc reg, sample, binary variable, predictor variable, anova, proc calis, simple linear regression, condition index, f-test, degrees of freedom, logistic regression, proc sgplot, standard deviation, odds ratio, logistic distribution, proc freq, response variable | source | 14 |
| 165 | Inventory Your Files Using SAS | Brian K. Varney | Whether you are attempting to figure out what you h ave when preparing for a migration or you just want to find out which files or directories are taking up all of you r space, SAS is a great tool to inventory and repor t on the files on your desktop or server. This paper intends to prese nt SAS code to inventory and report on the location you want to inventory | dataset, proc sql, sas enterprise guide | source | 5 |
| 166 | Investigating Host Plant Resistance to Aphid Feeding through SAS Text Miner | Ning Song, Jiawen Liu, Goutam Chakraborty, James A. Anstead |
The processes of host plant resistance to insect feeding and pathogen attack involve several complicated plant defense pathways comprising numerous regulations of pathogen-related gene expressions. The aim of this study is to examine the ethylene signaling defense pathway of melon plants. We present a novel way of applying text mining in plant resistance research literature reviews. SAS Text Miner is employed to analyze current literature emphasis with the purpose of identifying interesting and important research trends in the field of host plant resistance to insect attacks. We show that ethylene, jasmonic acid, salicylic acid, and calcium signaling pathways are major emphases in the plant-pathogen interaction field. Additionally, SAS Enterprise Guide is used to analyze gene expression changes in ethylene signaling pathway | sample, proc ttest, sas enterprise guide | source | 10 |
| 167 | Is Your Failed Macro Due To Misjudged "Timing"? | Arthur X. Li | The SAS macro facility, which includes macro variables and macro programs, is the most useful tool to develop your own applications. Beginning SAS programmers often don't realize that the most important function in learning a macro program is understanding the macro language processing rather than just learning the syntax. The lack of understanding includes how SAS statements are transferred from the input stack to the macro processor and the DATA STEP compiler, what role the macro processor plays during this process, and when best to utilize the interface to interact with the macro facility during the DATA STEP execution. In this talk, these issues are addressed via creating simple macro applications step-by-step |
macro variable, symbol table, local symbol table, macro facility, macro definition, global symbol table, call symput, binary variable, macro call, macro loop, %if-%then-%else, call execute, macro trigger, &variable, macro put, if-then-else, call symputx, sas log, proc step, chi-square test, put statement, character functions, macro function, do loop, run, compilation phase, execution phase, missing value, macro parameter, %macro parameters, proc sql | source | 22 |
| 168 | It's Now Your ProjectClean It Up and Make It Shine | Leonard Polak | Inherited projects may not be well designed. Though a new project owner may not have time to upgrade substandard code immediately, most inherited projects offer plenty of room for improvement. Developing department or company standards and best practices, and then applying these standards and best practices can result in code that is easy to understand, easy to maintain, and easy to transition. The examples in this paper are meant to promote improvements in productivity, consistency, and clarity | proc step, date variable, proc sql, proc format, sas enterprise guide | source | 10 |
| 169 | Its About Time: Discrete Time Survival Analysis using SAS Enterprise Miner | Sascha Schubert, Susan Haller, Taiyeong Lee |
The new survival analysis algorithm in SAS Enterprise Miner 7.1 provides analysts with an alternate approach to modeling the probability of customer behavior events. Traditional binary classification approaches provide a snapshot view of event propensities, while survival analysis can generate a time based function of event probabilities. This time-based view can help organizations to optimize their customer strategies by gaining a more complete picture of customer event likelihoods. This paper briefly explains the theory of survival analysis and provides an introduction to its implementation in SAS Enterprise Miner. An example illustrates the usage of this analytical algorithm using a customer churn data set | data mining, survival analysis, hazard function, data cleaning, regression analysis, model validation, sample, competing risks, training set, survival function, likelihood function, score code, churn, model training, stepwise selection, logistic regression, date variable, validation set, logistic distribution | source | 15 |
| 170 | Kass Adjustments in Decision Trees on Binary/Interval Target | Manoj K. Immadi | Kass adjustment, a commonly used option, maximizes independence between the two branches of a decision tree after any split, ut how well these adjustments work on interval and binary target variable s is under-researched. This paper describes split search algorithm in decision trees for selecting useful inputs, followed by comparing decision tree results with and without Kass adjustments for a binary target variable and an interval target variable. In addition, the paper also provides insights to Kass adjustments and Bonferroni correction in decision trees | response variable, proc hpsplit, post hoc test, missing value, data mining | source | 9 |
| 171 | Knowledge (of Your Missing Data) Is Power: Handling Missing Values in Your SAS Data Set | Theresa Schwartz, Rachel Zeig-Owens |
Before conducting any statistical tests it is important to check for missing values and evaluate how they may influence your study conclusions. This paper presents an overview of considerations that need to be made when confronted with missing data. We describe how to efficiently check for missing values, as well as investigate how SAS handles missing values and what can be done to correct for these missing values in your analysis | missing value, proc step, imputation, proc freq, proc mi, macro definition, binary variable, maximum likelihood estimation, multiple imputation, data science, likelihood function | source | 8 |
| 172 | Lean and Mean: Using Lean Six Sigma to Improve Your Analytic Processes | Robbie Reynolds, Rachel Alt-Simmons |
Many businesses have increased their analytic sophistication, with analytics becoming more pervasive in business processes. With the increase of analytic maturity, investments in the people, methodologies, and technologies needed to effectively manage those processes have lagged. Many analytic teams are tied up in managing production analytic processes, and often lack a framework for ensuring process quality and efficiency. In this session, we help analytic teams better manage these processes, as well as reduce errors and cycle time through a lean Six Sigma framework. The Life & Protection division of Transamerica puts the framework to work in making significant improvements across its marketing campaign optimization process | sample, quality control | source | 8 |
| 173 | Leave Your Bad Code Behind: 50 Ways to Make Your SAS Code Execute More Efficiently | William E. Benjamin Jr. | This laundry list of tips shows 50 ways to help SAS programmers make their code run faster and more efficiently. Topics include maximizing each DATA STEP, making simple tasks take less code, using macro variables to simplify maintenance, using built-in features, optimizing code to save disk space, using sorts for more than just sorting, and ways to make the program code just read better. Keywords: Base SAS, Code tips, Macro variables, procedures, conditional code |
macro variable, data step, txt, character variable, filename statement, dataset, proc step, %if-%then-%else | source | 22 |
| 174 | Let Oil and Gas Talk to You: Predicting Production Performance | Keith R. Holdaway | How do historical production data relate a story about the subsurface oil and gas reservoirs? Business analysts must perform accurate analysis of reservoir behavior using only rate and pressure data as a function of time. This paper introduces methodologies to forecast oil and gas production by exploring implementations of the AUTOREG, ESM, and MODEL procedures in SAS/ETS. The AUTOREG procedure estimates linear regression models when the errors are autocorrelated. The ESM procedure generates forecasts by using exponential smoothing models. Examples of the MODEL procedure arising in subsurface production data analysis are discussed. In addressing these examples, techniques for pattern recognition, implementing TREE, CLUSTER, and DISTANCE procedures in SAS/STAT are highlighted to explicate the importance of oil- and gas-well profiling to characterize the reservoir |
time series analysis, outlier, proc step, cluster analysis, confidence interval, missing value, proc distance, bootstrap, proc model, proc freq, regression analysis, proc cluster, data science, proc esm | source | 12 |
| 175 | Let the Data Paint the Picture: Data-Driven, Interactive, and Animated Visualizations Using SAS, Java and the Processing Graphics Library | Ryan Snyder, Patrick Hall |
This paper introduces a scalable technique that combines the data manipulation capabilities of Base SAS 9.2 with the Java Processing graphics library to generate customizable visualizations. With an instructive example, the reader is guided through connecting to the sashelp.iris sample data using a SAS/SHARE server to build a visualization applet. The explanation includes details for adding animation and simple mouse and keyboard interactions into the visualization. A basic understanding of Object Oriented (OO) programming is assumed. The included example was designed for the Windows platform; however, Java and Processing are designed to support many other operating systems. Additional reference material and sample visualizations are also included |
sample, dataset, standard deviation, label statement, sas base, proc step | source | 11 |
| 176 | Let the DATA Step Drive the Report Assembly Line | Sijian Zhang | Our data quality assurance (QA) reports are sent out to more than one hundred hospitals periodically. All reports have the same components with the information customized to each hospital. The components were assembled for each hospital one by one manually before. Running some simple code in the DATA STEP, we can speed up the process significantly. It works like generating the reports in an assembly line with limited programmer's intervention |
macro variable, dataset | source | 6 |
| 177 | Leveraging an Upgrade to Improve Metadata | Brent Whitesel, Angela M. Hall |
Upgrading to new hardware gives SAS administrators the rare opportunity to reorganize and clean up the SAS Folders metadata structure. For Highmark, partial promotion techniques were key to restructuring the metadata into a more secure and usable structure. This paper details the partial promotion process and the design considerations taken to ensure the reorganization maintained all the dependencies between objects | sas management console, dataset | source | 7 |
| 178 | Leveraging Analytics in Transportation to Create Business Value | Vijitha Kaduwela | In this paper, we discuss how Analytics is leveraged in the transportation industry to create business value. The real- life case studies presented in this paper cover Rail, Trucking and Airline. In each case study we discuss the business problems, the specific applications of Analytics to solve the business problems and the business value created in the process. We discuss the descriptive and predictive Analytics developed, and how they are used by the customers to gain a competitive advantage by optimizing capital expenditure, reducing operating costs, improving profitability, improving reliability, reducing risks and achieving compliance. We also discuss the specific SAS solutions and the products used for solving these business problems. We are planning to invite several of our clients to join this presentation | predictive maintenance, root cause analysis, data cleaning, proc hpsplit, data governance | source | 12 |
| 179 | Locally Visible, Remote Data and Format | Hsiwei Yu, Kamau Njuguna |
When in a workplace environment with mixed platforms, sooner or later one will come across the need to work in or access files in a host that is in a different environment. This paper shows how to make remote data available in the local environment. This way, one can perform interactive tasks such as browsing and querying as if everything is local, at one's fingertip, for more productive work | work library, proc step | source | 3 |
| 180 | Log Checks Made Easy | Yogesh Pande | It is Good Programming Practice (GPP) if a programmer checks SAS log for errors, warnings, and all other objectionable SAS NOTE. In order to successfully create tables, listings, and figures, the programmer must ensure that code is correct, and the accuracy of the code, assuming that the program logic is correct, solely depends on SAS log. Using SAS macro language and Base SAS , this paper will introduce a macro that will enable a programmer or statistician to check all SAS logs in a folder and tabulate the log issues by the name of the SAS log file using PROC |
sas log, macro call | source | 4 |
| 181 | Logging 101: Leveraging the SAS 9.3 Enhanced Logging Facility | Margaret A. Crevar | Logging and auditing are two of the most important aspects of security and utilization management for system administrators. SAS 9 contains an enhanced logging and auditing facility that provides the ability to better capture individual SAS job and workload utilization and performance. While applications such as SAS Management Console provide administrators with the ability to dynamically change logging levels and view log output, SAS programmers can take advantage of logging features using SAS 4GL language statements. This paper presents an overview of the enhanced logging facility in association with SAS Business Intelligence applications and independent ad hoc and batch jobs | sas enterprise guide, autocall macro, batch job, cpu time, sample, proc sql | source | 8 |
| 182 | Look Out: After SAS/STAT 9.3 Comes SAS/STAT 12.1! | Maura E. Stokes, Fang K. Chen, Yang Yuan, Weijie Cai |
Heralded by a new release-numbering scheme, SAS/STAT 12.1 comes loaded with new statistical capabilities. New development areas include model selection for quantile regression, quantile regression for censored data, and multi- variate adaptive regression splines. Epidemiologists will like the STDRATE procedure for computing direct and indirect standardized rates and risks for study populations. The FMM procedure becomes production and includes new features such as additional distributions. Other notable enhancements include modeling missing covariates with the MCMC pro- cedure and fitting Bayesian frailty models with PROC PHREG. This paper reviews highlights from earlier releases and describes highlights of SAS/STAT 12.1, slated for release during 2012 |
proc quantreg, regression analysis | source | 27 |
| 183 | Lost in Wonderland? Methodology for a Guided Drill-Through Analysis Out of the Rabbit Hole | Stephen M. Overton | Business information can be consumed many ways using the SAS Enterprise Business Intelligence Platform. SAS BI Dashboard can be used to report high level key performance indicators at-a-glance through the SAS Information Delivery Portal. Detailed web-based reports can also be surfaced from SAS Information Delivery Portal through SAS Web Report Studio. This paper will present an information system that integrates the functionality of these tools to answer business questions faster and with a greater understanding of the key drivers of business critical data. This paper will also present the data infrastructure needed to support this type of information system through the use of OLAP technology, effective data architecture, report linking, and information maps | star schema, sample, dimension table, raw data, sas enterprise guide | source | 12 |
| 184 | Macro Coding Tips and Tricks to Avoid "PEBCAK" 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 some general tips and tricks I use wne writing macros from my parameter checking scheme, which provides such users direct feedback via the SAS log to how I use comments to document code. 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 | macro variable, error message, proc step, macro function, macro loop, proc sql, %if-%then-%else | source | 8 |
| 185 | Macro Design and Usage in A Multi-Tier Architecture for ETL and Google Visualization API Integration | Manuel Figallo-Monge | Macros are useful for creating reusable, tried-and-true components that are part of an Extract, Transform, and Load (ETL) process. This paper examines several SAS macros that interact with one another to form a base ETL architecture, which consists of a macro to extract XML files from a web server, another to transform the XML files into SAS data sets, and yet another to load the data into a data visualization made available by the Google API for Data Visualizations. This data visualization is called a Motion Chart and can be integrated with SAS data sets, as shown by the architecture presented in this paper. The upshot is a highly interactive visualization that plays a dynamic "movie" and allows end-users to explore several World Bank indicators over time. These three primary macros either inherit other macros or can be extended with other macros to form larger applications | macro definition, web service, exception handling, sas programmer, excel | source | 17 |
| 186 | Magic Spells with SAS | Christopher J. Bost | Programmers often need to calculate spells, or the number of consecutive periods of time under different conditions. For example, you might need to calculate months of employment and unemployment, or days on and off a medication. This paper describes how to use multiple arrays and index variables to identify the beginnings and endings of spells, and how to summarize the number, minimum, maximum, and average length of spells | proc means, do loop | source | 5 |
| 187 | Make an Appropriate Page Break in a PDF When Using PROC REPORT | Xia Ke Shan | When using PROC REPORT to generate a PDF file, SAS will not split two group values if the current page can't hold the next group value any more. What we need is that when the current page can't hold the next group value we want to push the next group value into the next page (i.e. make a page break at appropriate position). This paper is to try to solv e this problem. SAS introduced the SPANROWS option in SAS 9.2 for PROC REPORT which does address this issue, but what if you don't have SAS 9.2 or you don't like the way the spanned rows looks on your report. Then you still might benefit from learning a technique to take control of page breaking |
proc report, proc step | source | 6 |
| 188 | Making Exploration of Nonlinear Models Fast and Easy Using JMP 10 | Don McCormack | Nonlinear models are frequently encountered in applied technological areas such as the semiconductor and pharmaceutical industries. Defect density and pharmacokinetic models are two examples where fitting a straight line to data is inadequate or inappropriate. Exploration of these models is often tedious and time consuming, and model comparison is difficult. Through its Nonlinear platform, JMP 10 introduces a quick and simple way to fit multiple curves and explore competing models, streamlining the discovery process. Two detailed examples are given using data similar to that found in practice | proc nlin, sample, outlier | source | 8 |
| 189 | Making it Happen: Using SAS to Implement Business Intelligence and Analytics in Small and Midsize Organizations | Richard R. Hallquist, Dragos Coles, Lorrie Gallego |
Business intelligence and analytics often seem to be the domain of large organizations with deep pockets. This paper abolishes that myth and shows you how you can get started in much smaller settings. Whether you are a midsize company or a small group within a larger organization, using SAS to implement business intelligence and analytics is an achievable goal. We use real-world scenarios to demonstrate how to extract and share insight from your data. This includes: formulating your goals using SAS Enterprise Guide to handle data management using SAS/STAT to build and score predictive models using SAS Enterprise Business Intelligence to build a dashboard and a PowerPoint presentation to show these |
sas enterprise guide, business intelligence, excel, regression analysis, logistic regression, line chart, feature selection, likelihood function, logistic distribution, sql server, data wrangling | source | 16 |
| 190 | Making Your SAS Data JMP Through Hoops | Mira Shapiro | Longtime SAS users can benefit by adding JMP to their repertoire. JMP provides an easy -to-use and robust environment for data exploratio n, graphics and analytics without the need for programming expertise. This paper will provide an introduction to JMP 9 with an emphasis on features that SAS users will find useful. During this presentation, users will learn how to read their SAS data, i mport EXCEL spreadsheets, transform their data, explore distributions, create reports and create sophisticated graphics all in the JMP environment. Users will be introduced to the tools within the JMP 9 environment that provide a pathway to quickly learn h ow to use the product and some of its unique features |
dataset, sample, multinomial logistic regression, proc hpsplit, logistic regression, logistic distribution, histogram, regression analysis | source | 19 |
| 191 | Managing Analytic Processes with Paired SAS Utility Macros | Zhongwen Huang, John Hou |
A pair of macros was developed to enhance user analytic process management in interactive SAS sessions. %_mStart(), the preprocessing macro, creates a relative path system independent from hard -coded absolute paths and is executable without program modifications when copied to a different location. In addition, %_mStart() can back up SAS source code automatically while the program is submitted for executing. The backed -up SAS program will have date and time stamp added to the original file name, serving the purpose of version control which is not d irectly available in a typical analytical SAS environment. Following the end of execution of the program, a post -processing macro %_mEnd() detects error and warning messages in a SAS log window and displays them in a pop-up window. Keyword: Macro; Relative Path; File Backup; Version Control; Error Detection | sas log, macro variable | source | 6 |
| 192 | Managing Multiple Languages with a Single Cube Using SAS 9.3 | Tony Zhang, Ying Jin |
Global organizations often manage business analysis efforts across a worldwide corporate enterprise. When OLAP technology is used to analyze organizational data, multiple OLAP cubes that contain different languages and cultural conventions are often utilized. Managing these cubes can be costly and resource intensive for an organization. It is now possible to manage multiple languages and conventions within a single cube using SAS Unicode Server. The example in this paper shows how source data from the United States, France, and China can be integrated. Using SAS Unicode Server, multiple languages are combined into a single cube. This provides reusable reporting that can be adapted to meet region specific needs. While multiple languages are often utilized in global organizations, those languages and conventions can now be combined into single cubes. This approach provides a united business analysis | dimension table, sas enterprise guide, excel, proc step | source | 11 |
| 193 | Managing SAS Technical Support in a Research Organization | Michael A. Raithel | Westat utilizes SAS software as a core capability for providing government and private industry clients with analysis and characterizations of their data. Staff programmers, analysts, and statisticians use SAS to manage, store and analyze client data, as well as to produce tabulations, reports, graphs and summary statistics. Because SAS is so widely used at Westat, the organization has built a comprehensive infrastructure to manage SAS technical support issues. This paper provides an overview of Westat's SAS technical support infrastructure, which provides a central resource for handling SAS software issues so that the programming and statistical staff can concentrate on providing clients with cutting edge analysis | sas programming, macro definition, sas programmer | source | 13 |
| 194 | Matching Data Using Sounds-Like Operators and SAS Compare Functions | Amanda Roesch | By combining both sounds -like operators and compare functions, SAS can quickly identify many intended matches between almost any two strings (I say almost because some typos and spelling mistakes are too drastic to even be caught by the human eye, let alone a computer program relying on logic). First, using the sounds -like (either SOUNDEX or =*) operator, SAS will pair every match that sounds even relatively close. Yes this will result in numerous pairings, but that's where compare functions are useful . They can parse the possibilities down for further evaluation. The c ompare functions that will be discus sed here include COMPARE, COM PGED and CALL COMPCOST, COMPLEV, and SPEDIS. Finally, using a sort and a well-devised cutoff value, SAS will give a final list of the most likely matches . This method not only increases the prospective matches, it uses logic and reason rather than manipulating the data itself | proc sql, character variable | source | 11 |
| 195 | Measuring Consumer Involvement Profiles as a Second-Order Construct Using SAS PROC CALIS | Anurag G. Srivastava, Vikram D. Suklani, Pranav Karnavat |
proc calis, proc step | source | 16 | |
| 196 | Mining and Merging DATAMONITOR and WRDS Databases with SAS | Niam Yaraghi, Rajiv Kishore, Rui Chen |
In this paper, we show how a DATAMONITOR database can be grouped to public and private companies based on ticker-symbol databases of NASDAQ, AMEX, and NYSE. Moreover, we show EVENTUS software output is cleaned and merged with the public companies databases | dataset | source | 5 |
| 197 | Mix and Match: Diversity in Displaying Data | Melissa Hill, Julie Kezik |
A programmer is often asked to 'run some frequencies' or 'put together a quick report' in order to share results with a group. Just as every scientist has a preferred output style (ie: graph, table, figure, list, etc.) every programmer has a preferred way of getting there. The results of posing this question to 5 colleagues produced a variety of approaches including the use of PROC MEANS, PROC TABULATE, PROC FREQ and PROC BOXPLOT as well as visualization techniques in JMP |
proc step, proc freq, proc boxplot, proc gplot, proc gchart, proc tabulate, box plot, proc contents, standard deviation | source | 12 |
| 198 | Mobilizing Your SAS Business Analytic Reports | Falko Schulz | This paper explains the steps involved in getting your SAS Stored Processes published to mobile devices such as iPhones and iPads. Specific HTML markup and JavaScript techniques allow a seamless integration of SAS output to be rendered on mobile devices. Considering the smaller screen real estate and input gestures, an intuitive touch-and- swipe user interface is used. This mobile interface allows the user to quickly browse for the desired stored process and view the rendered output. The paper provides an example toolkit including source code that can be used to get your business reports mobilized | macro variable, business intelligence | source | 13 |
| 199 | Modeling Heavy-Tailed Distributions in Healthcare Utilization by Parametric and Bayesian Methods | Joseph C. Gardiner | Distributions of healthcare utilization such as hospital length of stay and inpatient cost are generally right skewed. The extremes represent legitimate observations on patients who, because of the severity of their illness and need for medical intervention, have long in-stays and incur large costs. In this context we demonstrate the application of several parametric models for fitting heavy tailed data. Both maximum likelihood and Bayesian methods are used for estimation in certain Coxian phase-type models, mixtures of exponential distributions, and for comparison, the lognormal, log- logistic, Weibull, generalized gamma and generalized Pareto -including the standard Pareto and Burr distributions. We focus on the mean and percentiles of the response, and illustrate our methods with an empirical example on fitting models to hospital stays for acute myocardial infarction in the Nationwide Inpatient Sample of the Healthcare Utilization Project | sample, logistic distribution, likelihood function, proc step, maximum likelihood estimation, exponential distribution, logistic regression, bayesian statistics, regression analysis, gamma distribution, confidence interval, proc mixed, censoring, survival function, normal distribution, hazard function, proc phreg | source | 15 |
| 200 | Modernize with SAS Dashboards: Go Beyond a Collection of Gauges | Anand V. Chitale | A modern dashboard's ability to present data and information at both summary and detailed levels makes it one of the most powerful tools in a business user's kit, going beyond a collection of dials and gauges and meeting business needs in a practical and actionable way. The SAS BI Dashboard brings a powerful yet simple to use environment supporting the design characteristics of a modern dashboard. This paper showcases the characteristics of a modern dashboard and shows how you can achieve the same with SAS BI Dashboards discussing industry-specific dashboard examples | business intelligence, trend analysis | source | 7 |
| 201 | Monitoring the Variation in Your Multivariate Process: An Introduction to the MVPMODEL and MVPMONITOR Procedures | Blair Christian, Bucky Ransdell |
Complex processes in modern manufacturing and business environments can generate hundreds and even thousands of process measurements that vary over time. Early detection of process instability is critical for avoiding costly failures and minimizing risk. When the process measurements are correlated, multivariate statistical process monitoring meth- ODS are appropriate. Three new procedures in SAS/QC 12.1, the MVPMODEL, MVPMONITOR, and MVPDIAGNOSE procedures, implement methods that are based on a principal components approach to process monitoring, which was developed in the field of chemometrics. They provide T 2 and SPE charts, which are multivariate summaries of pro- cess variation. An example from social media sentiment analysis illustrates how the procedures work together and demonstrates the power of the methods for discovering and diagnosing unusual variation |
proc princomp, phase i, correlation, proc step | source | 13 |
| 202 | Moving Data and Results Between SAS and Microsoft Excel | Harry Droogendyk | Microsoft EXCEL spreadsheets are often the format of choice for our users, both when supplying data to our processes and as a preferred means for receiving processing results and data. SAS offers a number of means to import EXCEL data quickly and efficiently. There are equally flexible methods to move data and results from SAS to EXCEL. This paper will outline the many techniques available and identify useful tips for moving data and results between SAS and EXCEL efficiently and painlessly |
excel, data step, libname engine, proc step, proc export, sas enterprise guide, name range list, xls, proc import, dataset, delimited file, macro variable, txt | source | 22 |
| 203 | Multidimensional Scaling on ZIP Codes | Chao Huang, Xiangxiang Meng |
Postal code or ZIP code of the United States is a series of five digits initially serving the purpose of mail delivery, and is now extensively used for geographical partition in many fields . In SAS, the ZIPCITYDISTANCE function calculate s the physical distances between any pair of ZIP codes. The MDS procedure, based on the multidimensional scaling technology, translates the distance matrix of the ZIP codes into relational numeric values. This paper describes a statistical solution that separates the ZIP codes into limited levels by PROC MDS and the clustering methods in SAS/STAT |
proc mds, proc step | source | 7 |
| 204 | Multisheet Workbooks Using the ODS Excelxp Tagset | Cynthia A. Stetz | Most of us are engaged in providing data to information consumers at least some of the time, and by far the most often requested format is the Microsoft EXCEL workbook. As the capabilities of EXCEL have expanded, so have the requests for more and more sophisticated output, and the search for ways to generate this output with the least amount of human effort as possible. One of the SAS tools that I have recently started using to great effect is the ODS ExcelXP tagset. By harnessing some of the vast capabilities available within this tagset, in concert with the judicious application of macro code and Dynamic Data Exchange (DDE), I am now able to deliver nicely formatted, multisheet native EXCEL workbooks for any number of subsets of my data as might be desired |
excel, xls | source | 8 |
| 205 | MV_META: A SAS Macro for Multivariate Meta-Analysis | Julie A. Gloudemans, Corina M. Owens, Jeffrey D. Kromrey |
Meta-analysis of multiple outcomes and multiple treatments from a single study require more sophisticated models than the typical meta -analytic models that assume independence of effect sizes. Three different approaches have been suggested to accommodate dependent effect sizes: a multivariate multi-level approach (Kalaian & Raudenbush, 1996), a robust variance estimation strategy (Hedges, Tipton, & Johnson, 2010), and the traditional univariate random effects approach (Hedges & Olkin, 1985). This paper pres ents a SAS macro that calculates multivariate meta-analysis confidence intervals, mean effect sizes, and estimated effect size variances for each outcome variable given a sample of effect sizes and sample sizes. This paper includes a demonstration of the m acro, sample inputs and output, and an examination of the accuracy and precision of the three approaches based on a simulation study. Keywords: DEPENDENT EFFECT SIZES, RANDOM EFFECTS, CONFIDENCE INTERVALS, BASE SAS, | effect size, proc mixed, sample, sample size, fixed effects, proc calis, confidence interval, response variable, proc step, weighted mean, covariance, correlation | source | 15 |
| 206 | My Friend the SAS Format | Andrew H. Karp | The SAS System's FORMAT facilities provide a diverse and powerful array of tools to work with and present your data. By supplying a ra nge of resources to control how the values of data set variables are portrayed in your SAS output (i.e., your reports and analyses), SAS Format tools offer something for all SAS users, at all experience levels, and across the wide range of ways that the SAS System is used to report, manage, analyze and display data. Applying these capabilities to your SAS data sets, as well as the reports/analyses you generate from your data, enhance the visual appeal of your output, reduce ambiguity about the meaning of terms/definitions of values of variables, reduce coding and (depending on the size of the data set) processing time, and generally make your life easier as a SAS Software user. This paper starts with the basics and then discusses many ways you can take advantage of the many ways SAS Formats can be applied to your data, reports and analyses | proc format, proc step, proc print, work library, dataset, char., format statement, proc freq, sas 9, options statement, character variable, numeric variable, sas base, date variable, sysdate9, sas output, libname statement | source | 12 |
| 207 | Net Lift Model for Effective Direct Marketing Campaigns at 1800flowers.com | Roman Kubiak | This paper describes basic concepts, benefits and challenges of implementation of Net Lift Models in direct marketing campaigns at 1800Flowers.com. Net lift models predict which customer segments are likely to make a purchase ONLY if prompted by a marketing undertaking. The modeling work was conducted using stepwise logistic regression in SAS Enterprise Miner . The paper provides examples how net lift probability decomposition models leveraged differences between purchasers in test group and control group to predict which customer segments need a marketing contact and which customers segments are likely to make purchasing decision without a nudge | logistic regression, logistic distribution, regression analysis, data mining | source | 5 |
| 208 | New Features in SAS/OR 12.1 | Edward P. Hughes, Manoj Chari |
SAS/OR 12.1 debuts a broad range of new capabilities and enhanced features in operations research. This paper surveys these additions, emphasizing their benefits and their application to business problems, and especially to large business problems. Optimization improvements include faster problem generation and support for additional specialized solution methods in the OPTMODEL procedure. SAS/OR 12.1 can also exploit multicore computing in an updated version of the nonlinear programming multistart method and in underlying methods for the LP, QP, and NLP solvers. Other updates include an expanded set of constraint classes for the CLP procedure and enhanced SAS Simulation Studio support for large models and large experiments (many factors and responses) |
proc step, odds ratio, dataset, proc optmilp, constraint programming, network optimization, proc optlp | source | 11 |
| 209 | Not Just for Scheduling: Doing More with SAS Enterprise Guide Automation | Chris Hemedinger | SAS Enterprise Guide supports a rich automation model that allows you to "script" almost every aspect of opening and running SAS Enterprise Guide projects. While this is most often used with the built-in "scheduler" function to simply run a project or process flow unattended, you can also write your own scripts that automate much more. You can create new projects on the fly, supply values for project prompts, run individual tasks, export results, and more. You are not limited to the traditional VB Script method of automation; you can also use more sophisticated frameworks such as Windows PowerShell or Microsoft .NET. This paper describes the concepts behind automation for SAS Enterprise Guide, and provides examples to accomplish a variety of tasks using the different scripting technologies | sas enterprise guide, sas enterprise guide project, excel | source | 14 |
| 210 | ODS Document from Scratch | Kevin D. Smith | The ODS DOCUMENT destination is likely the most underutilized feature of ODS. The ODS DOCUMENT destination gives you the ability to customize the structure of your reports in ways that no other ODS features can. It enables you to store the actual ODS objects that are created when running a report , which you can then later rerun without invoking the procedures f rom the original report. You aren't limited to simply regenerating the same report; you can change the order in which objects are rendered, the table of contents, the templates that are used, macro variables, and system options. Even if you don't consider yourself an ODS wizard, don't be afraid to take a peek at the features of the ODS DOCUMENT destination in this paper because we'll be taking it from scratch |
proc step, proc contents, where statement, ods html, ods pdf, proc means, by group processing, order by | source | 28 |
| 211 | ODS PDF and RTF Application Development: Steps to Ensure Success and Examples of Useful Coding Techniques | Benno F. Kurch | For an ODS developer at a beginning or advanced level, creat ion of tailor-made ODS PDF or RTF reports is not an out-of-the-box, turnkey operation. Creat ion of such reports may require creat ive thinking and much experimentation. Implementing a larger font size, for example, when converting a line-printer ("legacy") report using a monospace font to an ODS report may require report re-design and consul tation with business review personnel. This paper will present some challenges encountered in a recent upgrade from legacy reports to more refined, formatted PDF and RTF output, and the recommended solutions to those chall enges. Taking time up front to think through potential issues can significantly reduce devel opment time and facilitate the production of properly designed ODS reports that will impress users with their professional look |
proc report, proc step, ods pdf, macro loop, macro definition, proc tabulate, macro variable, system organ class, %if-%then-%else, proc template | source | 12 |
| 212 | ODS Report Writing Interface Makes Our Reporting Simple and Better | Sijian Zhang | When we feel that a complex report cannot be generated directly from SAS, we usually get the data or some report components prepared in SAS, and then use other software, such as MS Word or EXCEL, to finish the reporting job. This situation has been changed since SAS 9.2. Taking one of our routine reports as an example, this paper will illustrate some useful features that the ODS report writing interface has, how syntaxes are applied, and how a complicated report can be done with simple code. With this new tool, our complex reports can be generated by just running the SAS program, which is much smoother and more efficient |
adverse events, output delivery system, if-then-else | source | 10 |
| 213 | Off the Beaten Path: Create Unusual Graphs with GTL | Prashant Hebbar | You open the latest edition of Time magazine and see a unique spiral bar chart of revenue shortfall by state, or your coworker sends you a link to an interesting population pyramid graph on a Web site and you ask yourself: "Can I do that using SAS"? Often, as it is for these two graphs, the answer is "Yes." With a little bit of data transformation and some creativity, you can generate spiral plots, conditional regression trees, and population pyramids using ODS Graphics, without a shred of annotate code. This presentation covers many tricks and tips to create unique graphs that grab the reader's attention, and also deliver the information effectively. Learn how to stretch the boundaries of what is possible with ODS Graphics |
series plot, scatter plot, bar chart, box plot, regression analysis, time series analysis, bubble plot | source zip |
26 |
| 214 | One at a Time; Producing Patient Profiles and Narratives | Joseph J. Hantsch, Janet E. Stuelpner |
Patient profiles can take many forms depending on the purpose for which they are going to be used. For data cleansing, it can be a data dump for each patient so that any data anomalies can be discovered. For medical review, the narrative type of profile would best. All of this can be done with Base SAS and macros to create the listings, formatted profiles, and narratives. An alternative is with a data visualization tool, JMP Clinical. Patient profiles are customizable and can display data from any combination of the core safety CDISC domains. A configurable patient narrative can be created for each subject who experienced a serious adverse event during the clinical trial | clinical trial, clinical data, cdisc | source | 6 |
| 215 | One Flip, Two Flip, Fat File, Flat File | Ethan Miller | Tired of getting data sets with multiple records per ID when you need a flat file for analysis? Tired of struggling with one-too-many merges? With just a small investment of time you too can easily create a flat file with one record per ID. Through the use of PROC SORT, PROC TRANSPOSE, SAS MACRO, and the DATA STEP, this paper examines how to create a flat file with customized variable names and labels for multiple time intervals. Through the use of SAS MACRO, the code shown in this paper creates a flat file, and your friends will be amazed at how easy it is. This paper was written for those with beginner-to-intermediate skills; the code was written using SAS 9.2 on a Windows operating system |
flat file, proc step, proc transpose | source | 6 |
| 216 | Optimized 1:N Case-Control Match Using SAS | Zhiwei Wang | proc sql, proc step, create table, full join, proc sort, macro loop | source | 5 | |
| 217 | Optimizing That Which cannot be optimized. Superfast SAS 9.2 + Searches and Fuzzy Linkage of Large Data Sets | Sigurd W. Hermansen | Some of the more successful innovations in the SAS System have percolated up through the SAS-L listserv and other sounding boards for SAS users. This review of large-scale search and fuzzy linkage methods focuses on questions and commentaries that have led to new techniques and methods. In turn, SAS has incorporated some of these innovations into later versions of the SAS System | proc sql, hash table, inner join, create table, view, proc step | source | 7 |
| 218 | Parallel Computing in SAS: Genetic Algorithms Application | Alejandro Correa Bahnsen, Darwin A. Amezquita, Andres F. Gonzalez |
Genetic Algorithms is a very powerful optimization technique that can be used in a wide variety of problems. But unfortunately the performance of this methodology relies heavily on computer power. We used Genetic Algorithms to select the architecture of a Multi -Layer Percept ron Neural Network and even though results indicated that it improves the predictive power of credit risk models, it is also very time consuming and computationally expensive. Because of this, we implemented a version of Parallel Genetic Algorithms in SAS using PROC CONNECT procedure. Results show that parallel computing can drastically reduce the total execution time of the genetic algorithm |
neural network, proc step | source | 5 |
| 219 | Perils of Ignoring Social Media | Ping Shung Koo | In 2010, we saw Social Media gaining influence over consumers while businesses remain apprehensive of engaging conversations online. Consumers have no qualms in making their voices heard on Social Media at all. Moreover, Social Media has become an avenue for consumers to seek more information before they make their purchase or patronize a service provider. Such behavior is even more pre valent with the coming of smartphones. Smartphones have developed a big group of "untethered" consumers that together with the abovementioned behavior, has offered "real-time" information to the communities they reside in and thus may affect buying decisio ns by the hundreds. In this paper, we are going to illustrate with examples of how businesses have responded to the challenges that social media brought to them | service provider, account | source | 3 |
| 220 | Physician Practice Pattern Variation: Using Data Mining and Predictive Modeling to Identify and Control Costly Treatment Patterns | David C. Ogden | Health insurance providers want to control the ever-increasing cost of health care while ensuring quality outcomes. Learn how SAS Enterprise Guide and SAS Enterprise Miner can be used to identify which physicians are generating excessive costs and which practice patterns are leading to cost-reduction opportunities. This analysis of physician practice pattern variation focuses on an automated layered reporting method for producing actionable | sample, outlier, regression analysis, sas enterprise guide, insurance claims | source | 12 |
| 221 | Practical Application of SAS Capabilities for Pharma Goaling and Performance Review | Ramya Purushothaman | This paper discusses a Pharma application that uses SAS to leverage internal and purchased information such as Sales and Marketing data including drug prescriptions, dollar and unit demand, target prescribers, and key customer account profiles to set goals, measure sales performance and identify trends across the geography. The capability of SAS to handle huge volumes of data seamlessly provides an advantage over other technologies. The reusability of SAS macros makes SAS solutions extensible across various brands, sales teams, and geo levels for reporting. All of these tasks are performed through familiar Base SAS procedures, functions, statements, and options. The paper explains how the business need is addressed using SAS by accessing, cleansing, and transforming information | proc step, sample, data validation rules, macro definition, data cleaning, proc sort | source | 16 |
| 222 | Practice on the SAS Scalable Performance Data Server Configuration for Maximum Performance | Danni Luo, David Bianchi, Stephen Bogacz, Greg Dorfner |
To ensure the SAS data ware house environment to be architected to meet the need of volume increases anticipated in the coming years, and to support our daily activities of analytical-oriented exploration, querying, data mining and reporting, we rebuilt our SAS Data Warehouse (SAS DW) in 2010 and 2011. In this project, we constructed six SPDS environments (Dev, QA, UAT, PROD, DR and BI), designed new file systems in each of them. We tuned SPD server parameters, conducted performance test and finally completed data migration. The way SPD Server configured can significantly affect performance. This paper describes our practice on production SPD Server configuration, demonstrates our test result in optimizing performance | cpu time, proc sql, dataset, sas enterprise guide | source | 7 |
| 223 | Predicting Electoral Outcomes with SAS Sentiment Analysis and SAS Forecast Studio | Jenn Sykes | With the wide proliferation of text-based data on the Internet, there are more opportunities for organizations to validate the claims made in their structured data. Here, I use a combination of SAS Sentiment Analysis and SAS Forecast Studio to predict the outcomes of popular elections when polling data is not readily available. I also use these tools to validate the outcomes of elections and check for potential instances of fraudulent election administration. I use examples from the real world of politics and the popular television show, American Idol | time series analysis, sample | source | 9 |
| 224 | Preparing for a SAS EBI Platform Deployment or Migration | Brian K. Varney | Getting a new SAS Intelligence Platform deployed o r migrated is an exciting time for a company. The p romises heard during the sales cycle and project demonstrat ions need to become actionable processes by the administrators, power users and information consume rs once the installation is complete. The reality i s that this can only happen with careful planning and preparation b efore, during and after the SAS platform installati on process. This paper will address how to plan and prepare for each phase of a SAS Intelligence Platform deployment/migration, such that, when the installat ion and configuration is complete; the platform can be leveraged in an organized manner. We assume that SAS Enterpr ise Business Intelligence platform has already been chosen | business intelligence, oracle access | source | 8 |
| 225 | PROC FORMAT, a Speedy Alternative to Sort/Sort/Merge | Jenine Milum | Many users of SAS software, especially those working with large data sets, are often confronted with processing time challenges. How can one reduce the amount of CPU required to retrieve specific data? In this paper, an "outside the box" approach using a matching method utilizing PROC FORMAT replaces the CPU-heavy Sort/Sort/Merge. It is ideal for situations when a key from one file is needed to extract data from another file. It is more apparently useful when at least one of the files is quite large. This method has been proven time and again to decrease CPU by 70% to 80% and is effective on all platforms utilizing SAS |
proc format, proc step, data step, cpu time | source | 5 |
| 226 | PROC REPORT Basics: Getting Started with the Primary Statements | Arthur L. Carpenter | The presentation of data is an essential part of virtually every study and there are a number of tools within SAS that allow the user to create a large variety of charts, reports, and data summaries. PROC REPORT is a particularly powerful and valuable procedure that can be used in this process. It can be used to both summarize and display data, and is highly customizable and highly flexible. Unfortunately for many of those just starting to learn PROC REPORT, the terms "customizable" and "flexible" often seem to be euphemisms for "hard to learn". Fortunately PROC REPORT is NOT as hard to learn as it appears. All you really need to have in order to get started is a basic knowledge of a few primary statements. In this introduction to PROC REPORT you will learn to use the PROC REPORT statement and a few of its key options. Several of the supporting statements, including COLUMN, DEFINE, BREAK, and RBREAK, and their primary options will also be covered |
proc report, proc step, class variable, proc means | source | 9 |
| 227 | PROC REPORT: Dynamic Column Headings | Jason Levy | proc format, proc step, call execute, proc report, macro variable, proc print, macro parameter | source | 2 | |
| 228 | Productivity Tips for SAS Enterprise Guide Users | Steven J. First, Jennifer F. First |
SAS Enterprise Guide is a versatile, revolutionary tool for everyone from novice analysts to experienced programmers. This paper focuses on tips for a wide variety of users. Topics include leveraging point-and-click functionality, producing quick ad hoc analyses, organizing SAS processes, increasing speed and accuracy in SAS, automating and streamlining processes, creating attractive reports and graphs, learning how to code what you want and avoiding what you do not, and leveraging new features available in SAS Enterprise Guide. We discuss many quick time-saving tips for analysts and programmers. This paper has something for any SAS Enterprise Guide user and also has great information for someone who has never used SAS Enterprise Guide but is interested in its capabilities | sas enterprise guide, sas programmer, proc step, proc tabulate, excel | source | 13 |
| 229 | Proficiency in JMP Advanced Visualization | Charles E. Shipp, Kirk Paul Lafler |
The premier tool for robust statistical graphics is JMP. It combines easy navigation, advanced algorithms, and graphical output you can trust. After a brief introduction of JMP navigation and resources within the JMP software, we take a tour of classical and modern advanced graphical capability of JMP. In the SAS Global Forum talk we introduce case studies that show the power of JMP using graphics and analysis. To conclude, we cover directions in training and JMP user exchange | source | 6 | |
| 230 | Programming the Provider Previews: Extreme Reporting in SAS | Christianna S. Williams, Louise S. Hadden |
Each month, we produce more than 15,000 data-driven reports (each report being three pages) for nursing homes, previewing their five-star ratings on various measures. These reports are produced entirely by SAS, including a macro toggle for test or production mode and the collection of 15,000 PDF documents into a single multi-gigabyte zip file. The elements of the program include SAS macros, the DATA STEP, the REPORT procedure, and the PDF statement in the SAS Output Delivery System (ODS). Each PDF document is named to easily upload into specific electronic mailboxes for each nursing home facility |
macro variable, output delivery system, proc step, ods pdf, proc report, macro definition, proc sql | source | 10 |
| 231 | Propensity Score Analysis and Assessment of Propensity Score Approaches Using SAS Procedures | Rheta E. Lanehart, Patricia Rodríguez de Gil, Eun Sook Kim, Aarti P. Bellara, Jeffrey D. Kromrey, Reginald Lee |
Propensity score analysis is frequently used to reduce the potential bias in estimated effects obtained from observational studies. Appropriate implementation of propensity score adjustments is a multi-step process presenting many alternatives for researchers in terms of estimation and conditioning methods. Further, evaluation of the sample data after conditioning on the propensity score informs researchers about threats to the validity of the adjustments obtained from such an analysis. This paper describes the steps required for a propensity score analysis, and presents SAS code that can be used to implement each step | propensity score, treatment effect, proc step, sample, causal inference, average treatment effect, dataset, proc calis, weighted least squares, regression analysis, response variable, potential outcomes, sensitivity analysis, proc ttest, logistic distribution | source | 11 |
| 232 | Put a Little Zip in Your SAS Program | Louise S. Hadden, Christianna S. Williams |
SAS programmers are frequently called upon to perform repetitive processes. To deal with repetitive processing SAS macros are indispensable. If a process calls for using software outside of SAS, it might be hard to figure out how to use SAS macros. This paper presents a solution to passing SAS macro code through to an external software call. A bonus is the ability to generate a listing of the resulting zip file and to output it to an external file within the SAS program so that processing done in batch can be easily checked. This example is run on a Microsoft Windows x64 server using WinZip. However, the process can be used on other platforms and with other software | macro variable, macro definition | source | 6 |
| 233 | Queries, Joins, and WHERE Clauses, Oh My!! Demystifying PROC SQL | Christianna S. Williams | Subqueries, inner joins, outer joins, HAVING expressions, set operators…ju st the terminology of PROC SQL might intimidate SAS programmers accustomed to getting the DATA STEP to do our bidding for data manipulation. Nonetheless, even DATA STEP die-hards must grudgingly ackn owledge that there are some tasks, such as the many- to-many merge or the "not-quite-equi-join," requiring Herc ulean effort to achieve with DATA steps, that SQL can accomplish amazingly concisely, even elegantly. Through in creasingly complex examples, this workshop illustrates each of PROC SQL's clauses, with particular focus on problems difficult to solve with "traditional" SAS code. After all, PROC SQL is part of Base SAS so, although you migh t need to learn a few new keywords to become an SQL wizard, no special license is required! |
proc sql, proc step, group by, inner join, where statement, order by, full join, select, missing value, by group processing, proc means | source | 37 |
| 234 | Quick and Dirty Microsoft Excel Workbooks without Dynamic Data Exchange or the SAS Output Delivery System | Andrea Wainwright-Zimmerman | There is a simple trick using the X command in SAS that allows you to write out your SAS data to an already formatted EXCEL workbook with graphs and/or pivot tables already built. This paper will describe how to accomplish this, as well as the limitations of this method |
excel, proc export, proc step, name range list | source | 9 |
| 235 | Quick Results with ODS Graphics Designer | Sanjay N. Matange | You just got the study results and want to get some quick graphical views of the data before you begin the analysis. Do you need a crash course in the SG procedures (also known as ODS Graphics Procedures) just to get a simple histogram? What to do? The ODS Graphics Designer is the answer. With this application, you can create many graphs including histograms, box plots, scatter plot matrices, classification panels, and more using an interactive "drag-and-drop' process. You can render your graph in batch with new data and output the results to any open destination. You can view the generated GTL code as a leg up to GTL programming. You can do all this without cracking the book or breaking a sweat. This hands-on-workshop takes you step-by-step through the application's features |
kaplan-meier plot, histogram, box plot, scatter plot, bar chart | source | 14 |
| 236 | Regular Expressions in SAS Enterprise Guide | Mark Tabladillo | In version 9, the SAS System introduces Perl regular expressions (sometimes known by the acronym PRX, the first three letters of these functions or call routines). However, previous versions of SAS already had regular expressions (known by their acronym RX, the first two letters of these functions or call routines). This presentation will describe specific functional and performance differences in these two exclusive regular expression strategies, and offer recommendations on when to use each strategy. The technologies will be compared using SAS Enterprise Guide 4.3 | sas enterprise guide, proc sql, prxposn, proc step, conditional do loop, macro variable | source | 10 |
| 237 | Reordering Values within Observations: Beyond CALL SORTC(N) | Adish Jain, Kate Bachtell |
PROC SORT orders SAS dataset observations by the values of one or more variables. CALL SORTC/N are new CALL routines in SAS 9.2 and above that reorder values within each observation (in ascending order only ) for the entire dataset. In short, PROC SORT orders data set observations vertically, while CALL SORTC(N) orders values horizontally within the observation. Although these new routines can be useful in some situations, this paper deals with a more complex data management task: sorting multiple groups of variables horizontally in descending order. In our case, these groups of variables are items for every child living in a household, which also must be linked back to household data using the child's age and position in the household |
proc step, proc sort, proc transpose, macro variable | source | 11 |
| 238 | Revenue Assurance: Using SAS to Identify Revenue Leakages | Ahmed Darwish, Sunil Goklani |
Would you be able to convince your executive management that all revenue and cost streams are free of monetary leaks? That all people, processes and systems are designed and executed correctly and that design oversights, system defects, and human mistakes fail to make their way into the organization? In a customer oriented industry, ramifications of allowing such anomalies, unchecked, could result in uncaptured revenue, strained relationships with vendors, and a poor customer experience. With increasing complexity and several components interacting in a large organization, issues are inevitable. In such settings, SAS becomes an invaluable tool for any Revenue Assurance team to begin to identify, quantify and address some of these anomalies | revenue assurance, data validation rules, account, proc report, proc step, proc sql, filename statement, macro variable | source | 9 |
| 239 | RTM and SASGSUB, the Power to Know What Your Grid Is Doing | Erwan Granger | With the release of SAS Grid Manager 9.2 M3, two new applications are changing the way that you interact with your SAS Grid. Platform RTM for SAS gives you a GUI to manage your Grid, as well as monitor its activity and performance. SASGSUB brings you the ability to submit a SAS program to the Grid using only the command line. Individually, each of these tools brings a whole new set of capabilities to the Grid. Together, they allow you to test your Grid configuration and validate its performance, so that you can maximize your investment in SAS Grid Manager technology and proactively manage your Grid environment | sas grid, sas enterprise guide, mysql | source | 26 |
| 240 | SAS and Microsoft SharePoint: Project and Resource Management Tools and Techniques for Large, Globally Distributed Programming Teams | Stetson Line | Most large companies employ a variety of in-house, off-shore, and service provider resources. Staff members often work in a variety of time-zones and can have different vacation and holiday calendars based on local, ethnic, and religious customs. How can managers effectively plan for resource demand peaks and valleys in such an environment? This paper presents a modest and scalable approach using SAS and MS SharePoint that provides a useful framework for both long-term forecasting and short-term load-balancing decisions in the management of globally distributed teams | sample, business intelligence, clinical trial, dataset, excel, proc step | source | 9 |
| 241 | SAS BI Content Syndication with the REST Framework | Mike R Vanderlinden | This paper discusses a method to make the SAS BI content available to other parts of your organization through the HTTP protocol. Data, Information Maps, Metadata, Reports, Stored Processes can be made available to any system that can talk HTTP, whether it's a browser on your desktop, a SharePoint website, a mobile device or anything else including a competitor's portlet. The REST framework is leveraged to provide the ability to reach SAS content with a zero-footprint client, as well as a powerful set of APIs to allow non-SAS developers to create rich interfaces | web service, excel | source | 14 |
| 242 | SAS Dates: Facts, Formats, and Functions | Debbie Buck | Among the first features of SAS that users learn is that SAS dates (and times) have unique characteristics. A SAS date isn't a "standard" numeric or character variable - when displayed it can look like character data but is stored as a number. The date of January 1, 1960, has a value of zero (0) - prior dates are negative numbers, those after have positive values. Despite SAS dates being part of the initial learning curve, there are a number of factors concerning dates that are frequently less than obvious to users, even experienced users. In this presentation, we focus on displaying/outputting SAS dates (formats), reading dates (informats), importing/exporting dates, calculating intervals and differences (functions), and extracting or combining portions of dates (more functions) | sysdate9, date variable, sas log, excel, dataset, input statement, character variable, proc step | source | 8 |
| 243 | 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 how SAS Enterprise Guide can help you? 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 SAS Enterprise Guide to access existing programs and enhance processing exploiting the enhanced development environment including syntax completion and built-in function help using 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 Enterpri se 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 SAS Enterprise Guide | sas enterprise guide, sas programmer, bar chart, excel, proc step, dataset, sas log, macro variable, work library, temporary dataset | source | 24 |
| 244 | SAS Enterprise Guide with SAS On-Demand for Academics: Everything You Need to Know After ""It's Free"" | AnnMaria De Mars | SAS On-Demand for Academics is a free "cloud -based" version of SAS softwa re available to university faculty, researchers, and students. In this hands -on workshop, participants will learn the administrative details (how and where to get it, set up student accounts, upload files), use SAS Enterprise Guide to solve both simple and complex statistical problems and practice troubleshooting some of the more common issues that arise when using SAS On - Demand for teaching. Because SAS On -Demand is now free to university researchers as well, participants will also run tasks that were used for two research projects, an analysis of survey data and a survival analysis | sas enterprise guide, logistic regression, regression analysis, logistic distribution, survival analysis, bar chart, proc means, correlation, proc freq | source | 20 |
| 245 | SAS In-Database Capability: Smart Architecture | Gaurav K. Agrawal | SAS capabilities are well recognized throughout various industries and businesses are getting valuable information from the data using SAS capabilities. However day-by-day data is increasing, which is posing urgent need of not only for a perfect architecture but also smart thinking to work effectively and efficiently. SAS also has recognizing this fact long back and working dedicatedly towards bringing innovative solutions to customers. SAS In-Database processing also one of the smart concepts to do analytics work and provide extremely valuable benefits to business in time. Using this methodology, not only Server resource utilizations can be decreased but this methodology also enables businesses to reduce time to market. This feature is a critical factor in current competitive environment. Companies already have their data in a data warehouse environment. This is becoming more prominent with the advent of specialized appliances in this technology stack | proc sql, data mining, in-database processing, proc step, data warehouse | source | 10 |
| 246 | SAS IOM and Your .Net Application Made Easy | Karine Désilets | At Statistics Canada, many statistical systems are implemented in a client -server development context allowing maximum use of SAS tools, software and solutions. This article focuses on development of Microsoft .Net client applications using SAS Integrated Object Model (IOM) to take advantage of the processing, analysis, reporting and data storage power of SAS. This article covers a number of best practices such as the different communication modes between Microsoft .Net and SAS, types of SAS code execution, parameter management, libref and fileref management, error management with raised event, data acquisition via Ado.Net, automated analysis of SAS log files and management of customized return codes between SAS and Microsoft .Net. Finally, a few broader topics associated with this work and future research projects are addressed | sas log, stored macro, sysrc, dataset, excel, connection string, libname engine, proc step | source | 16 |
| 247 | SAS Macros to Transpose SDTM Data Sets Automatically | Chunmao Wang | In pharmaceutical industry, when producing analysis datasets from clinical data, usually it is necessary to transpose variables for lots of domains. Individually manipulating each domain data would be time-consuming and error-prone. However, thanks to CDISC, the data structures are standardized so that it is possible to use macros to handle SDTM data sets automatically. Here we present a set of macros to transpose multiple columns for some domains. It not only saves programmers time but also improves data quality | macro variable, proc step, data step, adam dataset, clinical trial, cdisc | source | 7 |
| 248 | SAS OLAP Cube Tuning and Query Performance Optimization | Tatyana Petrova | SAS OLAP Server provides a powerful engine for processing multidimensional queries so that SAS OLAP users can get quick answers from their analysis requests. This engine's power relies on well-defined and properly tuned OLAP cubes. A cube should be designed keeping users' analytical needs in mind while following best practices for hierarchies and facts organization. As analysis patterns evolve over time, cube aggregations should be periodically re-aligned to provide the best foundation for querying processing. Luckily, with SAS tools, it is not hard to ensure that your OLAP tuning is up-to-date. This paper will help advanced OLAP users and administrators analyze OLAP cubes' usage and tune the cubes' aggregations set. It will also cover common factors that impact querying performance and include tips for query processing troubleshooting | sas management console, proc step, proc sql, raw data, options statement | source | 17 |
| 249 | SAS Output Delivery System ExcelXP Tagset: Customizing Cell Patterns, Borders, and Indention | Deepak G. Asrani | The SAS Output Delivery System ExcelXP tagset offers the most flexible way to write SAS output to Microsoft EXCEL. Standard customizable options in the ExcelXP tagset handle most of the EXCEL formatting very well. However, given the ever-growing formatting features in EXCEL, there is always a need to go beyond what is available. This paper describes how to do some of the EXCEL formatting that requires modification of the standard ExcelXP tagset. Specifically, this paper concentrates on customizing cell patterns, borders, and indention |
excel, ods excel, output delivery system | source | 13 |
| 250 | SAS Since 1976: An Application of Text Mining to Reveal Trends | Zubair Shaik, Satish Garla, Goutam Chakraborty |
Many organizations across the world have already realized the benefits of text mining to derive valuable insights from unstructured data. While text mining has been mainly used for information retrieval and text categorization, in recent years text mining is also being used for discovering trends in textual data. Given a set of documents with a time stamp, text mining can be used to identify trends of different topics that exist in the text and how they change over time. We apply Text Mining using SAS Text Miner 4.3 to discover trends in the usage of SAS tools in various industries via analyzing all 8,429 abstracts published in SUGI/SAS Global Forum from 1976 to 2011. Results of our analysis clearly show a varying trend in the representation of various industries in the conference proceedings from decade to decade. We also observed a significant difference in the association of key concepts related to statistics or modeling during the four decades | macro definition, proc freq | source | 10 |
| 251 | SAS Social Media or How I Learned to Love the Blog | Christopher Battiston | Being a newcomer to SAS, I was struggling with finding infomration specifically related to Canadian health-care data analysis. I spent a few days roaming through Google and stumbled across the Toronto Area SAS Society, which to me to the SAS Canada Community, which is a site dedicated to SAS users in the northern half of North America. It was here that I met some great people, learned a lot, and realised that social media is not the evil, privacy-breaching monster I thought it was. This presentation is an attempt to get others to see the benefits of sites like the SAS Canada Community | data science, sas enterprise guide | source | 3 |
| 252 | SAS Solutions Make HEDIS Measures Programming Easier | Qingfeng Liang | The Healthcare Effectiveness Data and Information Set (HEDIS), a product of the National Committee for Quality Assurance (NCQA), is one of the most widely used set of health care performance measures which are related to many significant public health issues such as cancer, cardiovascular conditions, breast cancer, diabetes, depression, etc. For managed care organizations (MCOs), HEDIS presents interesting information systems and programming challenges to MCOs due to complication of health insurance data and the complexity of the measures. The advantages of SAS programming power and its solutions make these challenges much easier to overcome. This paper will explain the major challenges we faced when we are trying to tackle these measures and, more importantly, we established powerful SAS solutions to overcome these pitfalls | proc sql, health insurance, proc step, sas base, create table, insurance claims, quality assurance, sql pass-through | source | 9 |
| 253 | SAS Tools for Cost-Effective and High-Quality Clinical Trial Reporting | Paul J. Novotny, Angelina Tan, Nathan R. Foster, Jeff A. Sloan |
Being able to rapidly create analyses on clinical trials reduces their cost by reducing turn around times. SAS reporting macros enable statisticians to be more productive and also results in their reports being more focused, accurate, and easier to present to investigators. Two reporting macros were developed and have become very popular SAS tools at the Mayo Clinic and the Alliance for Clinical Trials in Oncology. These macros are easy to implement but are also flexible enough to be useful on a wide range of studies | proc phreg, confidence interval, interval variable, time variable, kruskal-wallis test, clinical trial, macro call, rank test, median survival, odds ratio, sample size, sample, proc means | source | 10 |
| 254 | SAS User Groups: Why? How? | Rex Pruitt | This paper is about how to effectively establish and/or support a SAS User Groups at any level (I.e., Regional, Special Interest, Local, or Internal). SAS software is the mainstay of many successful organizations. However, leveraging the success of any SAS software installation is completely dependent upon its user base. Too often, organizations adopt, knowingly or not, the idea that if they buy a SAS software solution, their employees will use it effectively. Establishing and supporting SAS User Groups is proven to generate positive returns. Resulting interactions amongst SAS Users fosters healthy collaborative relationships that enhance their performance. In this paper, I will cite experiences and specific examples of SAS User Group 1) ROI, 2) support, and 3) establishment | group by, sas syntax | source | 9 |
| 255 | SAS/FSP Provides the Best Customizable Form Viewer | Peter Crawford | Drilling through data to examine exceptions (outliers or missing) you want to see a lot of detail about one row. You need a "form viewer". SAS/FSP software demonstrates, perhaps the best. There are aspects to SAS/FSP that are not matched among the related products i.e. SAS/AF, ViewTable, Web Report Studio, nor the downloadable extension for Enterprise Guide software that promises an FSEDIT screen. The feature least supported (in my opinion) among these alternatives is the customizable Form Viewer of |
excel, sas enterprise guide, label statement, dataset, work library | source | 14 |
| 256 | Schedule Your SAS Jobs and Go with the Flow: Maximize the Use of SAS with the Platform Suite | Laura G. Liotus | Creating a SAS job flow with Platform Suite for SAS in a Microsoft Windows environment, allows one to run processes unattended. SAS jobs are created and depl oyed in SAS Data Integration Studio. Job flows are designed and scheduled in SAS Management Console. These flo ws are run using Platform Suite for SAS which inclu des Platform Process Manager and LSF. The flows are cre ated with dependencies that include a job completio n status, file arrival or file existence. With a thoughtful plan, the user can get creative with the dependenci es in SAS Management Console and the flow capabilities of Pla tform Process Manager. An added feature of all of the tools includes job status notification. With proper desig n and planning, one can use technology to automate their SAS processes | sas management console, txt, sql server, proc sql | source | 12 |
| 257 | Seamless Reporting Automation through the Integration of JMP, SAS, and VBA | Rachel Poulsen, Raghunathan Chakravarthy |
The TiVo user interface (UI) is continuously enhanced for design. To monitor and improve UI performance, statistical tests are run on multiple test cases across various dimensions, and weekly reports are generated. The reports require the flexibility of EXCEL, the analysis power of JMP and SAS, and the reporting convenience of PowerPoint. This can be accomplished by integrating JMP, SAS ODS, and EXCEL VBA to create an automation tool that seamlessly runs the analyses, conditionally formats the table output, and generates reports in PowerPoint. JMP automates the statistical analysis, SAS ODS calls EXCEL VBA macros for the conditional formatting of EXCEL data sets, and SAS ODS generates reports in PowerPoint. This paper discusses the tasks involved in the process flow of the automation tool |
excel, conditional do loop, data science | source | 10 |
| 258 | Security Hardening for SAS 9.3 Enterprise BI Web Applications | Heesun Park | Web configuration for SAS 9.3 Enterprise BI Web applications need to be secured according to an organization's security policy. This paper examines the Web configuration security enhancement options and the protection of Web applications from security vulnerability attacks. Security enhancements for the configuration include single sign-on, integration with a reverse proxy security server, setting up a firewall, the use of SSL, and building a FIPS 140-2 compliant configuration. Implementation of SAS 9.3 Web application protection mechanisms for vulnerability attacks is explained along with the testing process based on the OWASP Top 10 list and the IBM AppScan penetration testing tool | proc sql, data cleaning | source | 11 |
| 259 | Selecting All Observations When Any Observation Is of Interest | Christopher J. Bost | A data set might contain multiple observations per person. Suppose you want to keep all observations for a person if at least one observation for that person meets certain criteria. This paper shows how to use PROC SQL to select all observations when any observation is of interest. The first method uses a subquery; the second method uses the GROUP BY and HAVING clauses. The SQL t echniques are compared and contrasted with a traditional DATA STEP match-merge |
proc sql, proc step, order by, group by, create table, proc sort, if-then-else | source | 5 |
| 260 | Selecting Unrestricted and Simple Random With Replacement Samples Using Base SAS and PROC SURVEYSELECT | David D. Chapman | This paper reviews different techniques for selecting unrestricted and simple random without replacement samples using BASE/SAS DATA STEP code, using procedures such as PROC SORT and RANK, and using the SURVEYSELECT procedure in SAS/STAT. An extremely brief review of random sampling basics is given t hat discusses the UNIFORM random number function and the use of the special SAS functions INT, CEIL, and FLOOR to select random samples in the DATA STEP. DATA STEP code for selecting unrestricte d random samples is given and explained. DATA STEP code for sele cting Bernoulli samples and two different approaches to selecting a simple random sample without replacement are presented and discuss ed. Simpler ways to select simple random samples without replacement using PROC SORT and PROC RANK are illustrated and examples given. An alternative to selecting a random sample in the DATA STEP is to use PROC SURVEYSELECT in SAS/STAT |
sample, proc step, random sampling, proc surveyselect, proc sort, sample size, proc rank, data step, set statement, dataset, parameter, do loop, sample weight, sas programming, sas function, unique key | source | 14 |
| 261 | Sending E-mails in Your Sleep | Andrew Hummel | In today's world of the mobile office it seems we are never totally disconnected from the workplace. An increase in global business has added to the need of always-available 24-hour a day communication. This often leads to an increase in working hours and a decrease in sleep. But SAS can help by running automated programs and sending e-mails with dynamic data and attachments around-the-clock. SAS can also monitor processes and send e-mail alerts when a problem is detected. At Delta Air Lines SAS has increased efficiency through automation and has allowed programmers to send e-mails while they are literally at home sleeping | macro variable, sas programmer, raw data, txt | source | 5 |
| 262 | Serving SAS: A Visual Guide to SAS Servers | Gregory S. Barnes Nelson | SAS has been running on servers since the late 1960s. Despite the emergence of PCs and workstation-class machines, SAS still reigns supreme on the server. With the introduction of the SAS platform 9 in 2004, the number and types of servers have g rown exponentially. As any good student of the DATA STEP will attest, knowing what SAS is doing is a critically important step in debugging and authoring efficient programs. In this paper, you will experience SAS through a visual tour - you will see what SAS is doing, how it works, which server is doing what, when the operating system plays a role, how security functions, and what happens to your data through th e entire process. Systems Architecture and AdministrationSAS Global Forum 2012 2 |
business intelligence, sas enterprise guide, sas 9, sample | source | 12 |
| 263 | SG Techniques: Telling the Story Even Better! | Chuck Kincaid, Jack E. Fuller |
The SAS Statistical Graphics (SG) procedures: SGPLOT, SGPANEL, SGSCATTER, and SGRENDER are exciting additions first put into production in SAS 9.2 that give easy access to some of the power of the Graphics Template Language (GTL). As good as these procedures are in helping you tell your analytical story, and they are good, there are techniques that can add extra value to your graphs. This paper will discuss four concepts introduced by William S. Cleveland in his book Visualizing Data - level ordering, slicing, banking, and stacking . For these techniques, we will explain the concepts, provide examples and outline the basic algorithms in this paper. The audience for this presentation is the statistician or business analyst who wants to tell their story even better |
class variable, macro variable, proc step, numeric variable, nominal variable | source | 14 |
| 264 | Sharing SAS programs between PC Server and SAS Drug Development | Magnus Mengelbier | clinical research, libname statement, macro call, macro variable | source | 7 | |
| 265 | Simple Version Control of SAS Programs and SAS Data Sets | Magnus Mengelbier | SAS data sets and programs that reside on a local network are most often stored using a simple file system with no version control, no audit trail of changes, and none of the benefits. In this presentation, we show you how to capitalize on the capabilities of Subversion and other simple, straightforward conventions to provide version control and an audit trail for SAS data sets, standard macro libraries, and programs without changing the SAS environment. Extending the interaction with Subversion using a standard SAS macro is also explored | data validation rules, audit trail, macro definition, clinical trial | source | 11 |
| 266 | Simplifying Effective Data Transformation Via PROC TRANSPOSE | Arthur X. Li | You can store data with repeated measures for each subject, either with repeated measures in columns (one observation per subject) or with repeated measures in rows (multiple observations per subject). Transforming data between formats is a common task because different statistical procedures require different data shapes. Experienced programmers often use ARRAY processing to reshape the data, which can be challenging for novice SAS users. To avoid using complex programming techniques, you can also use the TRANSPOSE procedure to accomplish similar types of tasks. In this talk, PROC TRANSPOSE, along with its many options, will be presented through various simple and easy-to-follow examples |
proc transpose, proc step, by group processing, repeated measures | source | 13 |
| 267 | Simplifying the Analysis of Complex Survey Data Using the SAS Survey Analysis Procedures | Varma Nadimpalli, Katie A. Hubbell |
Large sample-based surveys often have complex sample designs, with design features including stratification, clustering, multi -stage sampling, and unequal probability of selection of observations. The calculation of the associated sampling weights often involves nonresponse adjustments and raking to external control totals. The analysis usually includes descriptive statistics such as frequencies, means, totals and their standard errors. Using standard statistical software modules such as PROC SUMMARY, PROC FREQ and PROC MEANS to analyze such data results in underestimation of variance , as these routines assume that the data is from a simple random sample and do not take into account the complex nature of the sample. Now, however, the survey analysis procedures such as PROC SURVEYMEANS and PROC SURVEYFREQ that have been added to SAS/STAT software can compute variances that accurately reflect complex sample design and estimation procedures |
proc step, proc surveyfreq, proc calis, sample, proc freq, proc surveymeans, confidence interval, proc means, proc surveyreg, regression analysis, standard deviation, sample weight, proc surveylogistic | source | 14 |
| 268 | Social Media and Networking: The Ins and Outs | Todd Case | With companies being bought, sold, and the nature of the career track being redefined as job tenure is tending to be shorter, it's critical to build a name and a reputation for what specific skills you bring to your SAS community. Social media is a powerful way to present your contributions to employers as follows: getting your name and skills out there for potential employers and recruiters, controlling the presentation of your experience, recommending others and having them recommend you, understanding the relationships among employees within an industry, and networking and getting back in touch with programmers. This paper discusses these and other topics related to social media, with the intent being on using social media methods that can help you find the right candidate (from a manager's perspective) or a job (from a candidate's perspective) | source | 3 | |
| 269 | Solve the SAS ODS Data Trap in PROC MEANS | Peter Crawford, Myra A. Oltsik |
The first version of this solution to the ODS Data Trap in PROC MEANS was delivered at SUGI-31(2006). This update presents a revised version of the macro supporting additional features and eliminating a surprising error. Those who wish a practical solution to this ODS Data Trap will appreciate the enhancements that correct and simplify usage. Policies and impact of the macro are described for the more advanced audience who are interested in adapting the macro and its techniques for their own purposes |
proc step, output, proc contents, proc format, macro variable | source | 12 |
| 270 | Sometimes One Needs an Option with Unusual Dates | Arthur S. Tabachneck, Matthew J. Kastin, Xia Ke Shan |
Do you have date range analytical needs that you haven't been able to solve with the SAS interval and holiday functions? You have if those needs involved analyzing a date range that didn't start at the beginning, end or middle of a given month, inv olved holidays other than the US and Canadian holidays that are incorporated in the holiday function, or if you had to analyze data related to virtually any annual sporting event. This paper shows how the intervalds option, introduced in SAS 9.2 phase 2, c an be used to meet such needs and includes code for addressing fiscal years in Great Britain, Chinese, Hebrew and Islamic holidays, and for analyzing the various rounds of the NCAA March Madness basketball tournament | proc format, options statement, proc step | source | 14 |
| 271 | Sparklines in the DATA Step Graphics Interface from SAS/GRAPH | Murphy Choy | Sparkline is one of the latest visualization techniques that analysts are using to assist them in visualizing information. A pet invention by the famous visualization expert Edward Tufte, this form of visualization has incorporated many of this key ideas in the use of chart and diagrams to reinforce certain concepts and ideas. Sparkline can be implemented in SAS via certain ways. In this paper, we will illustrate the ease of creating sparklines with SAS DSGI techniques | time series analysis, data visualization | source | 7 |
| 272 | Speeding Up Discovery | John P. Sall | With fast hardware and multithreaded software, interactive exploration becomes feasible in larger problems. But that also means that it will tak e some resourceful techniques, combined with human judgment, to look across thousands of variables to find the relationships that are worth looking at | dataset, missing value, histogram | source | 13 |
| 273 | Splitting Data Sets on Unique Values: a Macro That Does It All | Hans Sempel | Sometimes it is necessary to split a data set into multiple data sets, depending on the unique values of a variable. After this task was requested several times at the Belastingdienst in the Netherlands, a macro was developed that could split every data set based on a variable. The results are a data set for every unique value of that variable. And, all it took was two DATA steps! | hash table, proc sql, macro variable, proc step | source | 5 |
| 274 | Standardized Difference: An Index to Measure the Effect Size between Two Groups | Dongsheng Yang, Jarrod E. Dalton |
Standardized difference scores are intuitive indexes which measure the effect size between two groups. Compared to a tLtest or Wilcoxon rankLsum test, they are independent of sample size. Thus, their use can be recommended for comparing baseline covariates in clinical trials as well as propensityLscore matched studies. In this paper, we show how to calculate sample standardized differences for continuous and categorical variables and how to interpret results. We also provide a SAS macro which performs the calculation without using the IML procedure |
effect size, sample, baseline characteristics, macro definition, confidence interval, sample size | source | 6 |
| 275 | Standardized Macro Programs for Macro Variable Manipulation | Chris Swenson | Creating macro variables for later use can help to create dynamic, powerful SAS programs. Creating those variables in a standard, consistent manner without causing any conflicts with other macro variables, however, can be repetitive, time consuming, and prone to error. Here we present macro programs that assist with generating macro variables with the intent to use the variables in specific ways later in the program. Each macro meets a set of criteria including allowing the filtration of input data (if possible); deleting conflicting macro variables; creating macro variables in a consistent, predefined manner; and reporting the result of the macro's activity | macro variable, macro loop, do loop, proc sql, proc step | source | 7 |
| 276 | Storage 101: Understanding Storage for SAS Applications | Bob Augustine | MOST OF THE PERFORMANCE PROBLEMS WE SEE HAVE TO DO WITH I/O. MOST OF THE TIME, WHEN DEPLOYING SAS APPLICATIONS IF YOU GET THE STORAGE PERFORMANCE RIGHT YOU GET THE SAS PERFORMANCE | sas grid | source | 4 |
| 277 | Straight from Memory: ADDR, PEEK, POKE as SAS Programming Tools | Paul M. Dorfman, William W. Viergever |
"APP" is an unofficial collective abbreviation for the SAS functions ADDR, PEEK, PEEKC, the CALL POKE routine, and their so-called "LONG" 64-bit counterparts - the SAS tools designed to directly read from, and write to, the physical memory in the DATA STEP and the SQL Procedure. APP functions have long been a SAS user's dark horse. Firstly, the examples of APP usage in SAS documentation boil down to a few tidbits in a technical report, all intended for mainframe system programming tasks, with nary a hint how the functions could be used for data management SAS programming. Secondly, the note about the CALL POKE routine in the SAS documentation is so intimidating in tone, that many a potentially receptive folk may have decided to avoid the allegedly precarious route altogether |
numeric variable, character variable, sas programmer, temporary array, proc sql, length statement, data governance, variable, variable list, dataset | source | 18 |
| 278 | Successfully On-Boarding SAS Analysts | Aaron J. Augustine | Finding and successfully on-boarding SAS Analysts can be challenging. Each new hire can have varying levels of work and SAS experience. As such, an On-Boarding process needs to be flexible to account for these situations while covering the necessary information to incorporate new hires into a company. This paper serves to outline a profile used for hiring SAS analysts and the training plan after they are hired. It was written for working in a research and development group within a CPG industry, but the structure could be applied to most new-hire situations. This paper is most applicable to Base SAS and SAS/STAT and could be applied to any operating platform |
sas analyst, sas 9 | source | 6 |
| 279 | Sudoku-Solving System by SAS | Setsuo C. Suoh | We have developed the Sudoku-Solving System by SAS. The SAS data set is supposed to be one of the most inconvenient data set to attack Sudoku puzzles because of its structure. Our system has four crucial factors: (1) how to depict the values of 81 boxes in a SAS data set environment; (2) how to attack Sudoku puzzles efficiently and smartly without using brute force; (3) the recursive technique employed for tree searching during the attacking process, using the %INCLUDE statement; (4) an option for recording all of the attacking process or history of a tree search. It consists of three SAS programs, one of which has 21 SAS macro definitions, and succeed in solving the hardest Sudoku puzzles available on the Internet within a reasonable time |
dataset, macro definition, proc print, proc step, macro variable, sas output, txt, sas log, %if-%then-%else, macro loop | source | 22 |
| 280 | SUPPQUALWhere's My Mommy? | Sandra VanPelt Nguyen | When using CDISC standards, there may be situations in which a field has been collected on a CRF or included in a vendor data transfer which seems to be clinically relevant but is not "topic" data belonging in a standard or custom SDTM domain (any of the three general observation classes). Since non-standard variables cannot be added to SDTM domains, this data typically gets mapped to a SUPPQUAL (Supplemental Qualifiers) dataset, but what do we do when there is not an obvious "parent" record corresponding to this data within one of the SDTM domains? This paper will provide a set of guidelines to use to determine how best to handle these situations | sdtm domain, adverse events, concomitant medications, cdisc | source | 8 |
| 281 | Take a Fresh Look at SAS Enterprise Guide: From point-and-click ad hocs to robust enterprise solutions | Christopher W. Schacherer | Early versions of SAS Enterprise Guide (EG) met with only lukewarm acceptance among many SAS programmers. As EG has matured, however, it has proven to be a powerful tool not only for end-users less familiar with SAS programming constructs, but also for experienced SAS programmers performing complex ad hoc analyses and building enterprise class solutions. Still, many experienced SAS programmers fail to add EG to their SAS toolkit. They face the barriers of an unfamiliar interface, new nomenclature, and uncertainty that the benefits of using EG outweigh the time spent mastering it. Especially for this group, (but also for analysts new to SAS), the present work attempts to orient new EG users to the interface and nomenclature while teaching them how to perform common data management and analytic tasks they perform with ease in SAS. In addition, EG concepts and techniques that focus on using EG as a development environment for producing end-user analytic solutions are described | sas enterprise guide, proc step, sas programmer, sas enterprise guide project, out=, proc sort, where statement, proc sql, if-then-else, sas programming, sas function, excel, dataset | source | 26 |
| 282 | Taking Full Advantage of sasCommunity.org: Your SAS Site | Donald J. Henderson, Arthur L. Carpenter |
sasCommunity.org is becoming a hub or clearinghouse for technical information related to the use of SAS software. The site is managed and run by SAS users for SAS users. It is free and open to all SAS users to browse. Any SAS user can contribute to the site - just create an ID in order to contribute new content or to expand upon existing content. sasCommunity.org is built on top of the same software as Wikipedia. So if you know how to use Wikipedia, you have a head start on using sasCommunity.org. This workshop will enable attendees to: Learn how to make use of this site Learn how to navigate the information contained on the site. Learn how to discover the wealth of its hidden treasures. Find out that there are a number of ways you can contribute, and discover how you too can quickly make a difference in this world-wide community of SAS users. Learn how even small contributions can enhance the site for everyone | macro function, macro facility | source | 10 |
| 283 | Tales from the Help Desk 5: Yet More Solutions for Common SAS Mistakes | Bruce F. Gilsen | macro function, macro variable, macro put, missing value, numeric functions, sas log, %if-%then-%else, macro quoting function, date variable, macro definition | source | 8 | |
| 284 | Techniques for Generating Dynamic Code from SAS DICTIONARY Data | Jingxian Zhang | Integrating the information from SAS DICTIONARY tables into programming helps create dynamic and efficient scripts to manage data sets. The purpose of this paper is to provide such techniques for generating dynamic code from SAS DICTIONARY tables. The author uses three macros to demonstrate how DICTIONARY tables -driven code is dynamically constructed via three approac hes: SQL select into macro-variable method, call execute method, and generate-and-include an external file method. These macros manage all data sets at the library level: capitalize all character data, dump all data into an EXCEL file, and query all charac ter data with certain length. Using the basic techniques discussed in the paper, SAS programmers can develop their own dynamic scripts to accomplish other tasks |
dictionary tables, call execute, proc sql, macro variable, sas programmer, select, excel, update statement, clinical trial, proc step | source | 7 |
| 285 | The Alarm Effect of Statistical Process Control Process in Automotives Manufacturing | Ying Wang | Statistical process control (SPC) is the application of statistical methods to the monitoring and control of a process to ensure that it operates at its full potential to produce conforming product. Under SPC, a process behaves predictably to produce as much conforming product as possible with the least possible waste. While SPC has been applied most frequently to controlling manufacturing lines, it applies equally well to any process with a measurable output. Key tools in SPC are control charts, a focus on continuous improvement and designed experiments. Much of the power of SPC lies in the ability to examine a process and the sources of variation in that process using tools that give weight to objective analysis over subjective opinions and that allow the strength of each source to be determined numerically | sample, sas enterprise guide | source | 8 |
| 286 | The Armchair Quarterback: Writing SAS Code for the Perfect Pivot (Table, That Is) | Peter W. Eberhardt, Louanna Kong |
"Can I have that in EXCEL?" This is a request that makes many of us shudder. Now your boss has discovered EXCEL pivot tables. Unfortunately, he has not discovered how to make them. So you get to extract the data, massage the data, put the data into EXCEL, and then spend hours rebuilding pivot tables every time the corporate data are refreshed. In this workshop, you learn to be the armchair quarterback and build pivot tables without leaving the comfort of your SAS environment. In this workshop, you learn the basics of EXCEL pivot tables and, through a series of exercises, you learn how to augment basic pivot tables first in EXCEL, and then using SAS. No prior knowledge of EXCEL pivot tables is required |
excel, dataset, proc step, proc tabulate, output delivery system, proc means, xls, raw data, macro variable | source | 36 |
| 287 | The DOW Loop: A Smarter Approach to Your Existing Code | Fuad J. Foty | Following the frequent use of the DOW-loop's logic, it became apparent to me that the DOW-loop structure could be easily extended to a greater percentage of existing SAS code. Some SAS code includes PROC SQL statements performed on large dat asets that can easily be replaced by a DOW-loop construct. Other SAS code include s PROC SUMMARY datasets that can be simply aggregated and merged into a new SAS dataset without having to leave the DATA STEP. In addition to reading the code in a more logical way, the process enables faster and more efficient performance. In this paper, I examine existing SAS code examples and discuss how to convert them to include this new and exciting DOW-loop |
dataset, by group processing, proc step, proc means, proc transpose, set statement, output, cpu time, missing value | source | 12 |
| 288 | The Emergence of Patterns in SAS Usage and Infrastructure | Chuck Kincaid, Mike R Vanderlinden |
It used to be that there was one particular look and feel to a SAS environment. Then platforms changed, editors changed, but for a long time the infrastructure remained the same. With the introduction of a comprehensive Bus i- ness Intelligence Platform, the expansion of SAS across the enterprise, and its integration with IT departments, new usage patterns have emerged. More infrastructure choices are available to satisfy the diverse roles required by co m- panies who aim for analytics maturity. This paper will present our analysis of the more prevalent patterns to date, and the pros and cons of each. The interested audie nce could include IT and business managers who are interested in the possible ways to deploy SAS and what fits best for their environment | sas enterprise guide, etl developer, sas programmer, sas administrator, data cleaning, data science, sas management console, data governance | source | 15 |
| 289 | The FILENAME Statement: Interacting with the World Outside of SAS | Christopher W. Schacherer | The FILENAME statement has a very simple purpose-creating a symbolic link to an external file or device. The statement itself does not process any data, specify the format or shape of a data set, or directly produce output of any type; yet, this simple statement is an invaluable construct that allows SAS programs to interact with the world outside of SAS. Through specification of the appropriate device type, the FILENAME statement allows you to symbolically reference external disk files, interact with FTP servers, send e-mail messages, and integrate data from external programs and processes-including the local operating system and remote web services. The current work provides examples of how you can use the different device types to perform a variety of data management tasks |
filename statement, web service, macro variable, input statement, infile statement, dataset, proc step, put statement, file statement, excel, missing value, delimited file, flat file, retain, txt, macro quoting function, fixed-width file | source | 29 |
| 290 | The Good, The Bad, and The Ugly | Toby Dunn, Kirk Paul Lafler |
The SAS System has all the tools users need to read data from a variety of external sources. This has been, perhaps, one of the most important and powerful features since its introduction in the mid-1970s. The cornerstone of this power begins with the INFILE and INPUT statements, the use of a single- and double-trailing @ sign, and the ability to read data using a predictable form or pattern. This paper will provide insights into the INFILE statement, the various styles of INPUT statements, and provide numerous examples of how data can be read into SAS with the DATA STEP |
input statement, raw data, infile statement, dataset, missing value, sas log, trailing @ | source | 12 |
| 291 | The Graph Template Language: Beyond the SAS/GRAPH Procedures | Jesse M. Pratt | The SGPLOT and SGPANEL procedures are powerful tools that are capable of producing many types of high quality graphs; however, these procedures have some limitations. What happens when one is asked to specifically produce a graph that these procedures cannot create? The Graph Template Language (GTL) is much more flexible when it comes to creating customized displays. This paper presents situations where the SGPLOT and SGPANEL procedures break down, then briefly introduces GTL, and finally uses GTL to generate the displays not possible in PROC SGPLOT and PROC SGPANEL |
proc step, box plot, proc sgplot, proc template, scatter plot, proc sgpanel, graph template language, histogram | source | 10 |
| 292 | The Path to Developing Your Organization's SAS Skills | Jonathan Boase, Donald E. Kros |
As organizations try to leverage data assets and analytics as a means to achieving competitive advantages, it becomes imperative to develop the talent of the employees. This paper outlines the methodology that we employ to collaborate with SAS to provide our users with a SAS training curriculum. This training path assumes little to no prior SAS experience and leads users through a series of courses that will teach them the SAS skills needed to reach that organizational goal | sas enterprise guide, excel, sample, data mining, sas programming, proc step, proc sql | source | 4 |
| 293 | 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, proc sql, lookup, hash iterator, sas programmer, conditional do loop, sample, order by, out=, sysrc, data merging, proc step | source | 17 |
| 294 | The SAS Log: A Wealth of Data and Job Flow Information | Steven J. First | There have been many programs and papers written about reading the SAS log for error checking and warnings. Although that information is valuable, we have found that the data messages that include filenames, row counts, and other information are a wonderful source of run-time metrics. This is where the real value of the SAS log is. These metrics, along with metrics provided by PROC SCAPROC, can be used to automate and validate run results, debug errors, provide job flow information and job optimization, and enable grids. This paper discusses the jobs that we have developed to use this neglected source of information |
sas log, proc step, sample, proc sql, record count | source | 8 |
| 295 | The Steps to Follow in a Multiple Regression Analysis | Theresa H. Ngo | Multiple regression analysis is the most powerful tool that is widely used, but also is one of the most abused statistical techniques (Mendenhall and Sincich 339). There are assumptions that need to be satisfied, statistical tests to determine the goodness fit of the data and accuracy of the model, potential problems that may occur in the model, and difficulties of interpreting the results. The first challenge is in the application of the techniques - how well analysts can apply the techniques to formulate appropriate statistical models that are useful to solve real problems. The second challenge is how to use a suitable statistical software package - such as SAS - to deploy the correct procedures and produce the necessary output for assessing and validating the postulated model | regression analysis, predictor variable, response variable, multiple regression, sample, correlation, influential observation, model validation, sample size, stepwise selection, outlier, data cleaning | source | 12 |
| 296 | The Systems Development Life Cycle (SDLC) as a Standard: Beyond the Documentation | Dianne Louise Rhodes | Has your company adopted the Systems Development Life Cycle (SDLC) as a standard for benchmarking progress on a project? Have they developed Word and other templates for documents created during SDLC? In three of my most recent positions, the stress was on completing the documents according to schedule, rather than emphasizing the work. The work involved cataloguing requirements, analyzing them and developing a good design document, and thoroughly testing the resulting code. When I first started programming in SAS, I was lucky to get any users requirements at all; it was always "I'll know it when I see it." But with the emphasis on the documentation, and not on the analytical work behind them, the project still falls behind schedule because of missed requirements | data cleaning, data validation rules | source | 5 |
| 297 | The Top Five Migration Considerations for Success | Andrew Mott | There are different ways to move house; you can empty the contents of your house into a truck and transport your belongings to a new house. Or, put your house with all its belongings on the back of a truck and relocate the house. Many people have performed the former, and some the latter; everyone has a different experience but a similar set of considerations. The same is true of migrating a SAS environment. There is a large number of SAS users who have migrated to SAS 9.2, and some to SAS 9.3, so there is a wealth of experience upon which to draw. This paper reviews a number of customer experiences to draw out the top five considerations for a successful migration | data cleaning, sas grid, sas management console, grid computing | source | 8 |
| 298 | The Traveling Salesman Problem: Optimizing Delivery Routes Using Genetic Algorithms | Sabah Sadiq | The purpose of this paper is to discuss the methodology of optimizing delivery route scheduling using genetic algorithms to solve the Multiple Traveling Salesman Problem (mTSP). The traveling salesman problem (TSP) is a combinatorial optimization problem where a salesman must find the shortest route to n cities and return to a home base. While the TSP restricts itself to one salesman, the mTSP generalizes the problem to account for multiple salesmen. With robust clustering and genetic algorithm procedures, SAS provides an ideal environment to solve this type of problem. In order to utilize SAS to optimize the delivery routes, clustering will be used, via PROC FASTCLUS, to transform the mTSP problem into a set of traditional TSP problems. These single TSP problems will then be solved using genetic algorithms, via PROC GA, and visualized using PROC SGPLOT |
proc step, proc fastclus, euclidean distance, cpu time, dataset, outlier | source | 6 |
| 299 | The Traveling Salesman Traverses the Genome: Using SAS Optimization in JMP Genomics to build Genetic Maps | Kelci Miclaus, Robert C. Pratt, Matthew V. Galati |
Agronomic research employs genomics to develop breeding strategi es that improve the disease resistance, health, and yield of agricultural crops. Plant genome complexity necessitates understanding how DNA markers across the genome are related by building genetic maps that group and order markers based on their inheritance. This biological challenge is an ideal application of optimization routines common in operations research. Optimal marker order can be formulated as the famous traveling salesman problem, where you follow a path through the genome that returns the short est genetic distance map. Minimum spanning trees can group markers using an undirected graph. Using algorithms from the OPTMODEL Procedure in SAS/OR implemented in a visual interface with JMP Genomics, biologists can quickly and optimally build genetic maps |
source | 10 | |
| 300 | The Use and Abuse of the Program Data Vector | James R. Johnson | Have you ever wondered why SAS does the things it does, or why your programs "get away with" the things that they do, or why SAS would not do what you wanted it to? A key operational component of SAS is the program data vector. Without it SAS would not function as we know it. With knowledge of how the program data vector functions, programmers can better understand how SAS works. This paper will help you understand how the program data vector works, how data steps use it, and how you can exploit, manipulate, and trick it. Many examples are included; the magic behind of the DOW-loop and other mysteries will be discussed. There is something in this paper for all levels of programmers from the very beginner to the most advanced | pdv, where statement, data step, set statement, keep statement, subsetting if, dataset, drop statement, rename statement, automatic variable, if-then-else, do loop, proc print, by group processing, character variable, proc step, retain, missing value | source | 23 |
| 301 | Three Simple Steps to Recovering and Inserting Data Using Do Loops, Indexed Macro Variables, and PROC SQL Update Statements | Stacey D. Collins | When recovering data records from paradata or log files, it can be tedious to parse and restore data to the correct variable within the data set, particularly when the number or order of variables which contain data can vary between records. This paper presents an effic ient way to use do loops and indexed macro variables to parse variable names and data from a paradata file, and load them into the main data set using PROC SQL UPDATE statements. This code is useful for restoring records one at a time, and does not require the programmer to hard code any variable names or variable orders for the purpose of inserting the data. The code is presented in SAS 9.2 in a windows environment |
macro variable, dataset, do loop, proc sql, update statement | source | 4 |
| 302 | Tips and Strategies for Mixed Modeling with SAS/STAT Procedures | Kathleen Kiernan, Jill Tao, Phil Gibbs |
Inherently, mixed modeling with SAS/STAT procedures, such as GLIMMIX, MIXED, and NLMIXED is computationally intensive. Therefore, considerable memory and CPU time can be required. The default algorithms in these procedures might fail to converge for some data sets and models. This paper provides recommendations for circumventing memory problems and reducing execution times for your mixed modeling analyses. This paper also shows how the new HPMIXED procedure can be beneficial for certain situations, as with large sparse mixed models. Lastly, the discussion focuses on the best way to interpret and address common notes, warnings, and error messages that can occur with the estimation of mixed models in SAS software |
proc step, proc mixed, proc glimmix, covariance, likelihood function, error message, estimate, covariance structure, sas log, random intercept, numeric variable, standard deviation, repeated measures, degrees of freedom, proc nlmixed, response variable, binomial distribution | source | 18 |
| 303 | Together at Last: Spatial Analysis and SAS Mapping | A. Darrell Massengill, Alexander Kolovos |
Spatial analysis and maps are a perfect match. Spatial analysis adds intelligence to your maps; maps provide context for your spatial analysis. The geostatistical tools in SAS/STAT software can model and predict a variety of spatial data. SAS Mapping facilities enable you to create rich visualizations from that material. This presentation introduces a new framework that combines SAS spatial analytics with SAS mapping. Examples demonstrate how you can use the SAS/GRAPH Annotate facility with the transparency specification (new in SAS 9.3) to combine a predicted spatial surface with traditional SAS/GRAPH maps, and show how to tap into the additional mapping resources of Esri software through the SAS Bridge for ESRI. These tools empower you to make more intelligent maps and more informative spatial analyses |
heatmap, macro variable, proc step, correlation, macro definition | source | 12 |
| 304 | Top Ten SAS Performance Tuning Techniques | Kirk Paul Lafler | The Base-SAS software provides users with many choices for accessing, manipulating, analyzing, processing and producing quality data and results. Partly due to the power offered by the SAS software and the size of data sources, many application developers and end-users are in need of guidelines for more efficient use. This presentation highlights my personal top ten list of performance tuning and coding techniques for SAS users to apply in their applications. Attendees learn DATA and PROC STEP language statements and options that will help conserve CPU, I/O, data storage, and memory resources while accomplishing tasks involving processing, sorting, grouping, joining (merging), and summarizing data |
dataset, cpu time, proc sql, where statement, pdv, options statement, proc step, if-then-else | source | 7 |
| 305 | Topic Discovery, Tracking, and Characterization of Social Media Conversations for Point of Origin and Dissemination Discovery | Barry deVille, Gurpreet S. Bawa |
Social Media conversations cover a wide range of human experience often in novel, sometimes revolutionary ways. The words that are used to express content can reflect mundane events or can sometimes reflect radical new ways of thinking and behavior. The correct identification of conversation in real time is essential in order to effectively respond or intervene in a timely fashion. This presentation demonstrates the interdependency between vocabulary use and social networks that is critical to understanding conversations in social media channels, where they came from, where they are going and what impact they are likely to have. We show how topics are extracted from social media and how they are identified and tracked. Examples of the language use, social networks, and associated impacts are demonstrated | proc step, group by | source | 12 |
| 306 | Tracking and Reporting Account Referral Activity Using Hash Tables and SAS Business Intelligence | James L. Beaver, Tobin E. Scroggins |
This paper demonstrates how Farm Bureau Bank uses the SAS hash object and the SAS hash iterator to track and report on new account referrals. New account referrals are tracked by agent representatives based on their location, sales territory, and manager. To handle these needs, an agent dimension table is created as part of a data warehouse. Examples show the use of the SAS hash object methods FIND(), REPLACE(), and OUTPUT() to add new records and overwrite and partition existing records in the agent dimension table. An example of the hash iterator and use of the LAST() method illustrates how to determine the last key in the table. Sample reports using SAS BI tools, including OLAP cubes and SAS Web Report Studio, are demonstrated | hash table, dimension table, fact table, slowly changing dimension | source | 10 |
| 307 | Traffic Lighting: The Next Generation | Julie VanBuskirk, Jennifer S. Harper |
Traffic lighting is a tool intended to let a reader quickly evaluate data and at-a-glance sort out good versus bad performance. As the name implies, traditional traffic lighting generally separates data into three categories highlighted with red, yellow, and green based on performance. However, if all the cells in a table are boldly colored in primary colors, the reader is unable to achieve their goal of easily sorting between good and bad results. In addition, colorblind users will not be able to even distinguish between colors. In this paper we will present the visual design changes made to our output and the SAS techniques behind them. We sought to reduce the amount of decoration in our tables and focus on what was important, the data! We employed a more subtle use of cell shading and borders to effectively draw the reader's eye to the results that most need their attention | proc format, proc step | source | 10 |
| 308 | Transforming Variables for Normality and Linearity - When, How, Why and Why Not's | Steven M. LaLonde | Power transformations are often suggested as a means to "normalize" univariate data which may be skewed left or right, or as a way to "straighten out" a bivariate curvilinear relationship in a regres- sion model. This talk will focus on identifying when transformations are appropri- ate and how to choose the proper transformations using SAS and new features of the ODS. There is also a discussion of why, or why not, you may choose the "optimal" transformation identified by SAS |
predictor variable, proc step, regression analysis, response variable, scatter plot, proc iml, histogram | source | 8 |
| 309 | Translating Foreign Language in SAS with Google Translate | Murphy Choy | Increased levels of global operation have resulted in companies having business in many different geographical regions of the world. This move necessitates the operation to be relatively localized and, thus, results in databases with extensive amounts of information to be captured in a foreign language. Often this creates difficulties in analytic projects that are done by English -speaking analysts operating in non -English-speaking environments. With the advent of Google Translate services, we will demonstrate how a simple command issued to Google can be used within SAS to generate the translated results | filename statement, dataset | source | 4 |
| 310 | Trend Analysis: An Automated Data Quality Approach for Large Health Administrative Databases | Mahmoud Azimaee | Stability across time is one of the important components in Data Quality Assurance Process. This paper talks about a SAS macro that has been designed to automate testing of stability across time as part of a larger data quality application package. Outlier analysis has been used for identifying unusual changes over time within large health administrative databases. The macro chooses the most appropriate model for smoothing the data curve/line. Potential outliers will be flagged on the scatterplot as suspicious points. Results will be presented only in a graphic format to be included in a Data Quality Report | outlier, data cleaning, macro definition, trend analysis, proc freq, proc step, %if-%then-%else | source | 9 |
| 311 | Twitter and Facebook Analysis: Its Not Just for Marketing Anymore | Jodi Blomberg | Think marketers have a hard time analyzing social media? Law enforcement has it even tougher. Crimes are constantly discussed all over Twitter and Facebook ---- and nobody's tagging them #crime. A vast multitude of other topics are also conversed about in these venues, making it no small task to capture intelligence from all of this text. We address two social media analytics applications for law enforcement. First, we show how to search social media sites for a specific set of people, gather up the publicly available data, and present it in a digestable form to the analyst. Second, we show how tracking events on Twitter can help us understand precursor activity to events such as riots. This will be useful to anyone using SAS to analyze social media | social network analysis, dataset, parsing, sample | source | 9 |
| 312 | Up Close and Personal with SAS Enterprise Guide 5.1 | Anand V. Chitale, Lina Clover |
SAS Enterprise Guide continues its evolution bringing great new capabilities enhancing usability and end user productivity. These capabilities range from enhanced capabilities for SAS Programmers, new data exploration capabilities with enhanced descriptive statistics, highly visual and interactive multidimensional data analysis, and enhanced query and analysis to achieve increased performance. This paper brings you up close and personal with the key highlights of SAS Enterprise Guide 5.1, demonstrating the application of these capabilities helping you learn how to get more from your data in less time than ever before | sas enterprise guide, proc means, sas programmer, sas management console, sas grid, time series analysis | source | 11 |
| 313 | Uploading Your IPEDS Data Electronically Using SAS | James D. Hume | This paper demonstrates how to use SAS code to produce a text file that can be used to electronically upload data to the IPEDS Completions Survey. Universities have to provide a large amount of completion data to IPEDS, and this can be a time-consuming task if data is manually entered in the system. Using a few DATA steps, this paper shows you how to upload all of your completion data in a matter of minutes, saving you a tremendous amount of time | txt, proc step | source | 6 |
| 314 | Use of Cutoff and SAS Code Nodes in SAS Enterprise Miner to Determine Appropriate Probability Cutoff Point for Decision Making with Binary Target Models | Yogen Shah | This paper illustrates the effective use of Cutoff and SAS Code node in SAS Enterprise Miner to change the default cut off value of predicted probability during decision making with binary target models. SAS, by default uses cut-off value of 0.5 to pred ict a binary outcome from predicted probabilities i.e. the chance of primary outcome is same as that of a secondary outcom e. A cut -off value of 0.5 is not always acceptable because the observed proportion of a primary outcome in a given population may not be always 50%. SAS Enterprise Miner provides Cutoff node to adjust probability cut -off point based on model's ability to predict true positive, false positive & true negative. We need to add small snippets of SAS code under "score" section of SAS Code node to account for the new cut-off value in scoring dataset. This paper introduces a Technique to analyze probability cut -off using SAS Enterprise Guide as well | data mining, type i error, sas enterprise guide, logistic regression, primary endpoint, response variable, logistic distribution, regression analysis | source | 6 |
| 315 | Use the Full Power of SAS in Your Function-Style Macros | Michael D. Rhoads | Function-style macros are macros that can be used within a SAS statement. Although such macros are extremely flexible, they can only use macro language code. So what do you do if you need a macro that can be embedded within a SAS statement, but the underlying task requires the execution of one or more DATA or PROC steps? You can now leverage the capabilities of user-written SAS functions to circumvent this limitation, meaning that your macros can now have the flexibility of a function-style interface while still being able to execute one or more SAS steps in the background. This paper describes the necessary techniques for developing such macros and provides examples of situations where they can be most beneficial |
proc step, proc sql, variable list, macro variable, dataset, macro function, macro facility, proc fcmp, sas function, macro definition, proc transpose, select, character variable, order by, sas programmer, dictionary tables, macro call, macro quoting function | source | 10 |
| 316 | Using Custom Data Standards in SAS Clinical Data Integration | Michael Kilhullen | SAS Clinical Data Integration is a product offering from SAS that enables you to collect and centrally manage metadata about how clinical data is transformed to published industry standards. However, many companies already have internal standards that enable greater business process efficiencies, or use standards that are required by an external source. This paper discusses how a custom standard can be added to SAS Clinical Data Integration and used in metadata management and data mapping features to transform data to the custom standard | data standard, sas base | source | 11 |
| 317 | Using DDE and VBA Techniques to Import Data from Microsoft Word Tables in Programming Specification Files into SAS | Mei Li, Zemin Zeng |
This paper will introduce a SAS application with Microsoft Wo rd for selecting table data in Word files and importing data from Word tables into SAS. Dynamic Data Exchange (DDE) and Visual Basic for Application (VBA) techniques used in the application will be presented in great detail. T he application significantly improves the efficiency and sustainability of automat ically converting Word tables to SAS data. The application is widely used at work for programmatically retrieving the comment s from the programming s pecification files during the creation of Data Definition documentation (define.xml/define.pdf) for New Drug Application (NDA) electronic submissions | dataset, adam dataset, sample, clinical trial, data science, excel, cdisc | source | 9 |
| 318 | Using Dynamic Views as a Supplement to SAS Security to Enhance Multiple Levels of Access Requirements to Row-Level Data Upon SAS Server Startup | Christopher Bresson, Marty Flis |
Many industries are challenged with regulatory requirements and customer demand for database access within the corporate environment. There must be restrictions in place to protect Personal Information and limit its access. This paper will show a supplement to SAS security to allow for row level access to sensitive data and restrict access based upon dynamic views driven by datasets at the time of autoexection as well as group settings in SAS Management Console. This supplement works well with user prompts in SAS Stored Processes, and SAS Enterprise Guide. The code has been tested in SAS 9.1.3 and 9.2 and on Windows, Unix and Linux operating systems. Benefits include: transperency to users, masking sensitive fields, and using dynamic views capable of handling multiple users with different access requirements for limiting observation displays, limiting libraries in the server list as well as the list of tables to be displayed for incorporating row level security or full data access | proc sql, where statement, libname statement, sas management console, sas enterprise guide, dataset, work library | source | 12 |
| 319 | Using Facebook to Engage SAS Users | Ping Shung Koo, Murphy Choy |
Social media has become a regular and common platform for organizations and businesses to engage relevant stakeholders. This is a very surprising phenomenon given that social media has taken its current form within the past few years. Because it is a new technology, there is no proper literature on how social media should be used to achieve designated objectives. As such, the use of social media is by trial and error, gaining lessons from the usage experience. Here we would like to share our experiences in using Facebook to engage members of the Singapore SAS Users Group (SUG). We ha ve not reached a stage where we are considered successful, but we hope that such sharing can help other SUGs around the world | sas base, business intelligence | source | 5 |
| 320 | Using PHREG for Model Selection to Explore the Time Taking Immigrants in the GB to Find First Employment and Cross Validating Frailty Terms | JuYin Helen Wong | Instead of modeling (repeated) cross sectional or panel data when comparing immigrants' employment patterns, this study employs Cox models to explore the length of time taking immigrants in the Great Britain (GB) to find their first employment to measure whether any transition-duration penalties (i.e., the length of time for a transition to take place) experienced by ethnic minority when compared to the majority groups. Frailty and stratifying terms were tested to take account of unobserved individual and geographic heterogeneities. Besides testing the default option of the frailty term in PHREG, three other frailty models were also tested to validate the significance test results of the frailty term. Keywords: survival analysis, Cox model, stratifying and frailty models, PROC PHREG, PROC NLMIXED, and PROC |
survival analysis, frailty model, proc step, proc phreg, sample, hazard function, main effect | source | 5 |
| 321 | Using Pre-Formatted Microsoft Excel Templates with SAS | George G. Mendoza, Subhashree Singh |
SAS has several different options for transferring data to EXCEL. However, exporting data into a pre-formatted EXCEL tem plate consisting of predesigned tables and charts can be challenging. Usually, there is some manual intervention required partic u- larly in a Unix environment. The solution to leveraging pre-formatted EXCEL tem plates in a server environment is the use of SAS Integration Technologies to serve as a bridge between base SAS and the EXCEL template. This paper will walk you through the steps required to setup this bridge so that you can begin to use EXCEL templates together with SAS in an automat- ed server environment |
excel, xls, dataset, proc sql, raw data, sample | source | 13 |
| 322 | Using PROC GAM to Forecast Claims Reserves in the Runoff Triangles | Ling Huang | Runoff triangle means the two-way tabulation according to the warranty start time and duration. Forecasting adequate claims and setting up suitable reserves in the runoff triangles is an important part of an insurance company. Traditionally the claims reserves are estimated by linear regression models such as chain ladder in practice. A more accurate and flexible model can be built by examining claims counts and claims size separately, and then combine the estimates to compute the total claims reserves. This paper focuses on fitting generalized additive models (GAM) with PROC GAM to predict claims counts and claims size separately, and then calculated the total claims reserves in the runoff triangles. The final model is validated on a test dataset to check how the accuracy |
proc gam, proc step, training set, case reserve, loss reserve, response variable, poisson distribution, estimate, degrees of freedom, regression analysis, proc reg, sample, proc freq, gamma distribution | source | 7 |
| 323 | Using SAS and LaTeX to Create Documents with Reproducible Results | Tim Arnold, Warren F. Kuhfeld |
Reproducible research is an increasingly important paradigm, and tools that support it are essential. Documentation for many SAS analytical products has long been created from a single-source system that embeds SAS code in LATEX files and generates statistical results from those files. This system is now available to SAS users as an open-source package, which is similar in spirit to Sweave (Leisch 2002) and SASweave (Lenth 2007). The system automatically generates the SAS program file, which includes SAS macros that use the ODS document for capturing the output as external files. Listing and Graphic tags display the captured tabular and graphical output. This paper describes how to access and implement the package, and it illustrates typical usage with several examples |
sas log, macro call | source | 16 |
| 324 | Using SAS and ZIP Codes to Create a Nationwide First Responders Directory | Andrew Hummel | SAS can calculate the distance in miles between two points by using ZIP codes and longitudinal and latitudinal data. This can be used to match potential disaster areas with the location of first responders based on their distance from the potential crisis area. Delta Air Lines serves more than 250 airports in the United States and has more than 10,000 pilots residing in all 50 states who could act as first responders. Using each airport's longitudinal and latitudinal coordinates along with each pilot's home ZIP code, a first responder's directory can be created listing each pilot living within a specified distance from each airport. This directory can be used to quickly match first responders to any emergency location in the United States | proc step, proc report | source | 4 |
| 325 | Using SAS Enterprise BI and SAS Enterprise Miner to Reduce Student Attrition | Matt T. Bogard, Chris James, Tuesdi Helbig, Gina Huff |
The true supremacy of the SAS Enterprise Business Intelligence Server is the ability to utilize the power of SAS Analytics to deliver real-time information to end users, who usually do not understand statistics, but have the ability to make a difference if they have easy access to the analyzed data. This paper describes the process of using SAS Enterprise Miner to develop a model to score university students based on their risk of attrition and deliver easy-to-understand | proc hpsplit, data mining, neural network, logistic regression, logistic distribution, regression analysis, business intelligence, feature selection, data cleaning, score code | source | 10 |
| 326 | Using SAS Enterprise Guide As a Powerful Regulating Weapon | Guangzhi Zhang, Xing Guo |
The ability to manipulate and analyze data from different banks in an effective manner is a big challenge for banking regulator. The purpose of this paper is to introduce how SAS Enterprise Guide and a powerful Enterprise Guide indicator calculation plug-in can help auditors to meet their complex regulating requirements especially data import from different databases and complex indicator calculation. With some customization efforts from SAS consultants, SAS Enterprise Guide become a powerful regulating analysis weapon, and it meets our ever changing regulating analysis requirements no matter how data is structured and how we need to manipulate it. This paper provides a detailed introduction on how we import data and define indicators through Enterprise Guide in an innovative way | sas enterprise guide, ledger, sample | source | 8 |
| 327 | Using SAS Enterprise Guide the Same Way as Base SAS and More | Rahman Sarker | This paper talks about a customized usage of SAS Enterprise Guide that will enable us to use SAS Enterprise Guide the same way as Base SAS; and some more unique usage of SAS Enterprise Guide that can benefit SAS programmers of all skill levels | sas enterprise guide, sas base, proc step, proc upload | source | 7 |
| 328 | Using SAS Enterprise Guide to Coax Your Excel Data In To SAS | Mira Shapiro, Kirk Paul Lafler |
Importing Microsoft EXCEL files into SAS can often be a challenge. Perfectly formatted EXCEL files with labels in the first row and idiosyncrasy -free, clean data is not usually the norm. We will show how to overcome many of the obstacles associated with creating SAS data sets from EXCEL workbooks by using various combinations of SAS Enterprise Guide 4.3's features. The import wizard, generated code, code suggestion mechanism, options, and the ability to preview the first section of a CSV file will all be shown as mechanisms for creating analytic data sets from EXCEL input |
sas enterprise guide, excel, numeric variable, dataset | source | 15 |
| 329 | Using SAS for the Design, Analysis, and Visualization of Complex Surveys | Sharon Lohr | SAS PROC SURVEYMEANS, SURVEYREG, SURVEYSELECT, and other members of the SURVEY family are powerful tools for designing surveys and analyzing data from complex surveys. We present an overview of the procedures and their standard uses. We then present examples from complex surveys to illustrate how recent developments to these procedures allow them to be used for advanced applications such as graphing complex survey data and making inferences employing the bootstrap |
sample, histogram, proc step, bootstrap, regression analysis, proc calis, box plot, proc surveymeans, survey weight, bubble plot, degrees of freedom, proc surveyreg, random sampling, proc quantreg, sample weight, data visualization, proc surveyselect, relative frequency, proc freq, macro definition | source | 15 |
| 330 | Using SAS GTL to Visualize Your Data when there is Too Much of it to Visualize | Perry Watts, Nathaniel Derby |
In many of the SAS Institute publications about the new ODS statistical graphics, there is an introductory statement that defines an "effective" graph as one that reveals "patterns, differences and uncertainty that are not readily apparent in tabular output " (Kuhfeld, 2010 ; Rodriguez, 2008 ; Rodriguez and Cartier, 2009 ). Developing a good graph becomes a challenge, however, when input data map to crowded displays with overlapping points or lines. Such is the case with the Framingham Heart Study of 5209 subjects captured in the sashelp.heart data set and a series of 100 cumulative booking curves for the airline industry. In addition, interleaving time series plots can be difficult to interpret, and patterns can be missed when lattice plot panels are charted out -of-order. In the paper, transparency, layering, data point rounding, median calculation, and color coding are among the techniques that are evaluated for their effectiveness to add visual clarity to graphics output |
histogram, scatter plot, proc step, series plot, time series analysis, contour plot, order by, proc template, proc freq, graph template language, missing value, proc sgpanel, proc means, raw data, proc sgplot, nominal variable | source | 21 |
| 331 | Using SAS Macros to Remediate Existing SDTM Data Sets for New Drug Application (NDA) Submission | Yanwei Han | This paper presents the development of macros to prepare existing SDTM data sets for submission within a New Drug Application (NDA). Within the scope of this process was (1) the late-stage checking of SDTM data sets under very tight timelines, and (2) the creation of a single SAS program to make changes to the SDTM data sets for every study within the submission | phase i, proc sql, cdisc | source | 4 |
| 332 | Using SAS ODS Graphics | Chuck Kincaid | SAS has a new set of graphics procedures called ODS Graphics. They are built upon the Graphics Template Language (GTL) in order to make the powerful GTL easily available to the user. SGPLOT and SGPANEL are two of the procedures that can be used to produce powerful graphics that used to require a lot of work. This upgrade is similar to the ease-of-use upgrade in output manipulation when ODS was first published. This present ation will teach the audience how to use PROC SGPLOT and SGPANEL and the new capabilities they provide beyond the standard plot. By using these new capabilities, any user can tell the story better |
proc step, series plot, proc sgplot, numeric variable, proc sgpanel | source | 18 |
| 333 | Using SAS PROC MIXED to Fit Health Policy-Based Hierarchical Models | Lori L. Miller, Fred Wilds |
SAS procedure PROC MIXED is a flexible procedure for fitting complex hierarchical linear models and calculating corresponding statistics. Documentation for PROC MIXED, however, remains complex and the defaults are often not appropriate. Using PROC MIXED does not preclude the need for substantial data processing to prepare for modeling, data analysis, and circumventing conversion issues due to the model including 36 public school groups and three or more student race/ethnicity and socioeconomic status categorical variables. This paper demonstrates: the process of aptly structuring a custom data set without use of array applications; creation of dummy coded variables for each categorical variable; and integration of the dummy coded variables, while omitting a chosen reference group, into PROC MIXED. Employing this approach expands the use of PROC MIXED |
proc mixed, proc step, nominal variable, proc model, data wrangling, data science, dataset | source | 7 |
| 334 | Using SAS to Build Web Pages Linking Photographs with Google Maps | Arthur S. Tabachneck, William Klein |
Many of us at least occasionally take a picture with either a digital camera or cell phone. And some of us, be it for work, a SAS user group, or for personal use, end up posting some of our pictures on a web page. JPG files can contain a lot more information than just the picture s one sees, including such data as when and where pictures were taken, titles, picture heights and widths, and many of our camera's settings. This paper includes all of the base SAS code needed to create web pages that show one's pictures, in date taken order, with links that, when clicked, will cause Google Earth to show you to where the pictures were taken | macro variable, sas base | source | 12 |
| 335 | Using SAS to Extend Logistic Regression | Dachao Liu | Logistic regression is widely used in analysis of categorical data especially data with variables that have binary responses. It can be used in many fields where discrete responses and a set of explanatory variables coexist. For instance, it can be used in survival analysis which often uses data with variable having values of 'success' and 'failure'. We can let logistic regression do more things for us by extending it. This paper will discuss under what circumstances we extend logistic regression by using SAS | logistic regression, regression analysis, logistic distribution, conditional logistic regression, proc step, predictor variable, response variable, likelihood function, proc phreg, hazard ratio, proc reg, proc genmod, proc logistic, sample, sample size, binary variable | source | 8 |
| 336 | Using SAS to Get a Date: Integrating Google Calendars API with SAS | William G. Roehl | Google offers a powerful API that allows the more analytically and visually powerful SAS to interact with and manipulate calendar data. By combining the ease of Google calendaring with the power of SAS, businesses can operate more efficiently, increase customer satisfaction, and analyze their calendar data in nearly limitless ways. This paper illustrates how SAS can be used to create, modify, and directly interact with Google Calendar data via API calls. Using these calls, it is possible to retrieve available calendars; import all calendar data such as date, time, location, etc.; as well as add, delete, and modify preexisting calendars and their entries. This data can then be analyzed in any number of ways available through the power of SAS | macro call, macro variable | source | 9 |
| 337 | Using SAS to Manage SAS Work Area Usage | Urvir Palan | SAS platform administrators alw ays feel the pinch of not having enough space in t he SAS w ork area, even though they plan for the w orst. There are multiple approaches to tackl e this problem, but one of the better ones is to initiate the alert mechanism as soon as you see the first sign of fire | data step, sas administrator | source | 4 |
| 338 | Using SAS to Measure Airport Connectivity: An Application of Weighted Betweenness Centrality for the FAA National Plan of Integrated Airport Systems (NPIAS) | Hector Rodriguez-Deniz | The US Federal Aviation Administration (FAA) estimates that $52.2 billion will be available over the next five years (2011-2015) to fund airport infrastructure developments. Because one of the main objectives is to reduce congestion and del ays, there is a need to acknowledge the importance of connectivity (measured with a centrality indicator) when establishing funding priorities. Currently, the FAA does not do this. In this paper, we expand an existing SAS/IML implementation of betweenness centrality to handle passenger -weighted airport networks, construct a graphical representation of the US air transport network from airline ticketing data, test the module to identify hub airports, and produce stylish output using SAS GMAP. Performance a nd complexity considerations of the new algorithm are addressed |
proc step, proc iml, sample, proc sql, proc gmap | source | 12 |
| 339 | Using SAS to Move Data between Servers | John E. Bentley | In today's data management environments, it is not unusual for UNIX servers to be dedicated to a particular department, purpose, or database. As a result, a data analyst using SAS often works with multiple servers, each with its own data storage environment. Adding complexity, for security reasons our IT partners often do not make it easy for the servers to talk to one another. How do we transfer data between servers? SAS provides several solutions. This paper shows ways to use SAS Data Transfer Services and Remote Library Services combined with Remote Compute Services to move data between servers. Code samples are provided. All levels of SAS programmers and SAS Enterprise Guide users working in multiple server environments should find the paper useful | dataset, ftp, macro variable, proc step, sas base, flat file, filename statement, big data, cpu time, traffic data, work library, data cleaning | source | 14 |
| 340 | Using SAS/GRAPH to Create Visualizations That Also Support Tactile and Auditory Interaction | Ed Summers, Robert E. Allison, Julianna Langston, Jennifer Cowley |
Concepts, ideas, and analyses are commonly presented as graphics that are optimized for visual consumption. Does that mean blind students and professionals are out of luck? Not anymore. This presentation demonstrates best practices for multimodal visualizations for blind users of the iPad and other touchscreen mobile devices. Multimodal visualizations allow blind users to interactively explore visualizations through touch, discover details through sound, and comprehend the essence of the visualizations without vision. The presentation explores how to use SAS/GRAPH to create data visualizations that are optimized for access by blind users. For brevity, this paper will focus on scatter plots. However, the live presentation will demonstrate best practices for a variety of common charts, plots, and maps. We will demonstrate how to create multimodal visualizations using SAS macros that encapsulate the best practices |
source | 11 | |
| 341 | Using the New Features in PROC FORMAT | Richard D. Langston | This paper describes several examples using functions as labels within PROC FORMAT definitions. Also described is the new feature allowing for Perl regular expressions for informatting data, as well as other new features in PROC FORMAT for SAS 9.3 |
proc format, proc step, date variable, proc fcmp, sysdate9 | source | 7 |
| 342 | Using the SAS ODS Report Writing Interface to Create Clinical Study Reports | Rui Duan | exact test, csr | source | 11 | |
| 343 | Utilize Dummy Datasets in Clinical Statistical Programming | Amos Shu | Due to collectability or other issues, some clinical trial reporting tables like physical examination, demographic characteristics, and some efficacy tables usually need to be partially made up in some way in the real clinical practice world. Creating dummy datasets is an effective way to improve programming efficiency in these situations. This paper discusses six ways to utilize dummy datasets in clinical statistical programming | clinical programming, efficacy summary | source | 5 |
| 344 | Utilize Red Hat Kernel Virtual Machine to Enable Your Development and Test Environment to Peacefully Co-Exist with Your Production Environment | Bob Augustine, Barry Marson |
As x86-64-based servers, such as the HP ProLiant, have become less expensive, increases in performance have enabled businesses to seek ways to more effectively utilize THEM WITHIN THEIR ENTERPRISES. Another industry trend has created the need for businesses to be able to further subdivide larger proprietary servers that have utilized using hard and VIRTUAL PARTITION TECHNOLOGY. In order for a migration from these proprietary architectures to be viable, there is a need to offer a migration methodology for virtualization technologies to be made aVAILABLE ON COMMODITY HARDWARE. Because of SAS software's unique I/O footprint methods of virtualization utilizing shared resources, typically enabled via a HYPERVISOR HAVE BEEN AVOIDED | proof of concept, key performance indicator | source | 17 |
| 345 | Van Gogh Your Data: Data Visualization Methods with SAS Business Intelligence | Natalie Parsons, Scott W. McQuiggan |
From the halls of academia to the walls of museums, data visualization is an art form with ever increasing prominence. But how do we take data visualization concepts from these expert designs and create real-world business solutions? Using methods from distinguished data visualization experts and SAS BI, this paper presents contemporary techniques to visualize your data and simplify over-complicated reports with ample examples for both novice and expert report writers. We explore the utility of simplicity in BI reports, design guidelines that capitalize on understandings of visual perception, and optimizations for report consumption on various display media. While these techniques are applicable across industries, we will focus our examples on illustrations using data fundamental to the education industry | bar chart, data visualization | source | 6 |
| 346 | Variable Selection for Multivariate Cointegrated Time Series Prediction with PROC VARCLUS in SAS Enterprise Miner 7.1 | Akkarapol Sa-ngasoongsong, Satish T. Bukkapatnam |
Vector Error Correction Model (VECM) has recently become a popular tool for economic anal ysis and forecasting for multivariate cointegrated time series. However, o ne problem of this type of model is over -parameterization issue. Traditional method to address this problem is to impose weak exogeneity assumption on variables. Assuming unknown structural relationship among variables, imposing this assumpt ion alone may not be sufficient , especially in case of large number of hypothesized variables . This paper presents a variable selection method for multivariate cointegrated time series prediction using variable clustering procedure (PROC VARCLUS) in SAS Enterprise Miner 7.1. The empirical results show that long-run equilibrium relationship among variables selected by variable clustering procedure can be reasonably identified. The comparison of forecasting performance with other classical time series techniques demonstrates a significant improvement on a prediction accuracy |
time series analysis, correlation, proc step, feature selection, unit root, proc varclus, null hypothesis, vecm, proc arima, data mining, sample, demand forecasting, partial autocorrelation, normalization, proc princomp, proc varmax | source | 12 |
| 347 | Visualizing Spatial Data Using SAS and Google Static Maps | Jizhou Fu, Yanwei Zhang |
We present a new tool that enables SAS users to overlay various spatial patterns on Google Static Maps in a straightforward manner. The tool relies on the graphical functionality from the DATA STEP Graphical Interface, and is structured in a way that a variety of graphical forms or spatial information from different data sources can be combined effectively. We give an extensive discussion of the design of the tool and related background information, and provide detailed guidance on its use. Also, the usefulness and the flexibility of this tool are demonstrated through separate applications to survey field listing maps and crime distribution visualizations |
dataset, statistical software | source | 9 |
| 348 | Wake Up Your Data with Graph'n'Go | Christopher Battiston | Graph'n'Go is a quick and easy way to visualize your data, from simple bar charts to complex, multi-graph dashboards. With a lot of the functionality being hard to find, this presentation aims to navigate the new user through GnG to find the hidden gems. These will enable the newcomer not only to produce graphs effectively, but will also teach them about SAS programming | sas enterprise guide, sas base | source | 6 |
| 349 | Wallet and Share of Wallet Estimation: A Flexible Methodology | Shira Witelson | In this paper, we will demonstrate how Wallet and Share of Wallet estimation, widely used in the credit card industry for marketing and sales strategy, can be applied in a range of industries . The methodology presented consists of a series of steps, beginning with primary research, and progressing to the use of SAS-enabled unsupervised learning method, predictive modeling and statistical test techniques. The result is a flexible means for estimating the most realistically attainable Wallet of a customer and thereby calculating the customer's Share of Wallet. Definitions: Internal Spend: The dollar amount the customer spends on the company's products and services. External Spend: The dollar amount the customer spends on other providers' products and services. Wallet: The Total Spend (Internal and External) made by the customer with the company and other providers, so long as the company provides one of the services purchased | proc step, proc fastclus, proc glm, correlation, sample, proc genmod, regression analysis | source | 15 |
| 350 | Weighted Portmanteau Tests Revisited: Detecting Heteroscedasticity, Fitting Nonlinear and Multivariate Time Series | Thomas J. Fisher | In the 2011 SAS Global Forum, two weighted portmanteau tests were introduced for goodness-of-fit of an Autoregressive-Moving Average (ARMA) time series process. This result is summarized and extended for use as a diagnostic tool in detecting nonlinear and variance-changing processes such as the Generalized Autoregressive Conditional Heteroscedasticity process. The efficacy of the weighting scheme is shown in simulation experiments and analysis of stock market data. The statistics are easy to implement in SAS and source code is provided. Lastly, the versatility of this methodology is discussed for a fitted GARCH, Vector ARMA, and other time series processes | time series analysis, correlation, autocorrelation, partial autocorrelation, sample, null hypothesis, proc step, proc print, gamma distribution, moving average, proc arima, monte carlo, multivariate analysis, proc nlin | source | 12 |
| 351 | What Do You Mean, Not Everyone Is Like Me: Writing Programs for Others to Run | Jack Hamilton | Writing programs that other people will run can be difficult, and programs that might be run on multiple operating systems and versions of SAS can be even more difficult. And if you're like me, a program you wrote a year ago might as well have been written by someone else. This presentation discusses some of the challenges of writing programs to be run by others, and some possible guidelines to get around those challenges - some based on beyond-the basics programming, some based on programming style. Some of the topics that will be discussed are: DOW loops, hash objects, regular expressions, choosing system options, and minimizing log messages | options statement, missing value, proc step, proc printto, macro variable, output delivery system, macro definition, macro function | source | 11 |
| 352 | What Is a SAS Mentor and Why Do I Need One? | Stanley E. Fogleman | A SAS mentor can be a valuable resource to junior programmers just starting out, or programmers who may have used other high level languages and ar e just starting to use SAS. The paper will focus on preparing a plan for a hypothetical junior programmer, with the goal of becoming a more proficient programmer ove r a one or two year period. SAS, unlike some other languages does not lend itself to being self-taught and some things that might look like an obvious choice can actually lead to performance penalties. The most important thing that mentors can provide is guidance - for things worth studying more and things that might be interesting, but you migh t only use infrequently | sas programmer, output delivery system | source | 5 |
| 353 | What to Do with a Regular Expression | Scott Davis | As many know, SAS has provided support for regular expressions for some time now. There are numerous papers that expose the basic concepts as well as some more advanced implementations of regular expressions. The intent of this paper is to bridge the gap between the very beginning and the advanced. In the past you might have solved a programming problem with a combination of SUBSTR/SCAN and other functions. Now a regular expression may be used to greatly reduce the amount of code needed to accomplish the same task. Think of this paper as a recipe or guide book that can be referenced for some real-life examples that will hopefully get you thinking about ways to create your own regular expressions | prxposn, sas programmer | source | 6 |
| 354 | What's New in SAS Data Management | Nancy A. Rausch, Michael Ames, Wilbram Hazejager |
The latest releases of SAS Data Integration Studio and DataFlux Data Management Platform provide an integrated environment for managing and transforming your data to meet new and increasingly complex data management challenges. The enhancements help develop efficient processes that can clean, standardize, transform, master, and manage your data. Latest features include capabilities for building complex control processes, additional in-database ELT transformation capabilities, big data capabilities, enhanced features for monitoring data and processes, and new features for unstructured data access, master data, and metadata management. This paper provides an overview of the latest features of the products and includes use cases and examples for leveraging their combined capabilities | proc sql, update statement, delete, where statement, proc step, data validation rules, data governance, data profiling, create table | source | 13 |
| 355 | What's New in SAS/ACCESS and Process Improvements That You Apply to Your DBMS | Howard Plemmons Jr., Michael Ames |
This paper provides an overview of major new features in SAS/ACCESS and looks at process improvements that can help with DBMS performance. The information provided will help with SAS execution performance |
proc sql, proc step, hive, implicit sql pass-through, sql pass-through, create table, libname statement, proc print, flat file, libname engine, proc contents, time series data | source | 14 |
| 356 | Wheres the LISTING Window? Using the New Results Viewer in SAS 9.3 | Cynthia L. Zender | Are you making the transition from earlier versions of SAS to SAS 9.3? Did you wonder what happened to the LISTING window? Do you like the new cumulative HTML file, but wish you could figure out how to get a different behavior for selected pieces of output? This paper outlines several techniques for adjusting the behavior of the SAS windowing environment Results Viewer using SAS 9.3. Along the way, you will also see examples of the new Output Delivery System (ODS)-related options that allow you to establish destination and style defaults in your configuration file. By the time this presentation is over, you will understand how to work with the new Results Viewer behavior. Several job aids are provided that outline the alternatives |
proc step, work library, ods html, proc reg, proc freq, proc print, proc report, sas output, output delivery system, sas log, sas enterprise guide, ods pdf, macro function, sas programmer | source | 38 |
| 357 | Why Does SAS Say That? What Common DATA Step and Macro Messages Are Trying to Tell You | Charles E. Mullin, Kevin Russell |
SAS notes, warnings, and errors are written to the log to help SAS programmers understand what SAS is expecting to find. Some messages are for information, some signal potential problems, some require you to make changes in your SAS code, and some might seem obscure. This paper explores some of these notes, warnings, and errors that come from DATA STEP and macro programs. This paper deciphers them into easily understood explanations that enable you to answer many of your questions |
sas log, character variable, substr function, error message, numeric variable, proc step, proc import, macro facility, macro variable, macro function, macro definition, sample, options statement, proc contents, dataset, stored macro, macro parameter, %macro parameters, input statement, length statement, put statement, character functions, &variable, by group processing, format statement, macro quoting function, missing value, txt, %if-%then-%else | source | 17 |
| 358 | Working the System: Our Best SAS Options | Patrick Thornton, Iuliana Barbalau |
This paper provides an overview of SAS system options and discusses the best options. These are options that are beneficial, interesting, or both. There are many ways to work the SAS system by changing the settings of one or more of hundreds of system options. These settings can have sweeping effects that influence DATA steps (REPLACE and MERGENOBY=), the Log (MSGLEVEL=), data sets (VALIDVARNAME), listing output (NODATE, NONUMBER, ORIENTATION=, and FORMDLIM=), and other destinations (PDFSECURITY=). Option s ettings are invaluable for working with format catalogs (FMTSEARCH=), and they are essential for using, developing, and debugging macro programs (MPRINT, MLOGIC, MPRINT, SYMBOLGEN, and, new to SAS 9.2, MCOMPILE and MCOMPILENOTE) | options statement, macro variable, proc step, proc options, macro function | source | 10 |
| 359 | Write Once, Run Anywhere! How to Make Your SAS Application Speak Many Languages | Mickael Bouedo | Do you have SAS users worldwide? Do you want your SAS application to be useable in many languages? SAS 9.3 internationalization features will get you there efficiently. If you want to offer your SAS application in 2, 4, or 10 languages, SAS internationalization features help you write your code once so it can run in many languages. Internationalization is the step which generalizes your product to be language independent. Localization is the second step adapts the product to meet the needs of different languages and cultures. This paper describes how to successfully internationalize and localize your SAS programs and make them ready for the world | dataset, macro function, options statement, index, character functions, macro variable, string functions, macro facility, proc sort | source | 17 |
| 360 | Yes! SAS ExcelXP WILL NOT Create a Microsoft Excel Graph, but SAS Users Can Command Microsoft Excel to Automatically Create Graphs from SAS ExcelXP | William E. Benjamin Jr. | The SAS ODS Tagset ExcelXP creates .xml output, and .xml output cannot contain graphs. So how can SAS programmers get graphs into your EXCEL workbooks? One way is to build them in EXCEL yourself. This paper shows you how to create data using SAS, and then command Microsoft EXCEL to read the data, create a graph or fully reformat a worksheet, without putting an EXCEL macro into the output EXCEL Workbook. And the program will do it all while you watch, including for multiple sheets in a workbook. The SAS code, and EXCEL code, shown is a fully integrated system to create and format macro-free EXCEL workbooks, using SAS 9 (SAS 8 if Internet downloads are available) and EXCEL 97 and above |
excel, xls, proc print, proc step | source | 19 |
| 361 | Yes, We Can... Save SAS Formats | John F. Ladds | If you share the results of your SAS programs with anyone; you likely use SAS formats. Each time you run your program, you recreate the formats. This format recreation takes time, it adds to the length of your log, the formats do not change once you have created them and SAS issues warnings that the formats already exist. Nonetheless, you still create them over and over again. Well, did you know that you can easily create your formats once, save them and then re-use them again and again? If you are feeling generous, you can even share them with others | format statement, dataset, work library, libname statement, proc format, proc step | source | 6 |
| 362 | Yet Another Sudoku Solver, PROC FCMP | Matthew J. Kastin, Arthur S. Tabachneck |
There have been many different Sudoku solvers written using various SAS tools (e.g., DATA STEP, PROC CLP, PROC OPTMODEL with MILP Solver, and PROC SQL). This paper introduces yet another tool, PROC FCMP, which can be used to solve Sudoku puzzles using recursive functions. A unique feature of this solver is the inclusion of the ability to determine whether a puzzle has a unique solution, which is a necessary step toward actually generating Sudoku puzzles directly with SAS. Additionally, the paper presents a comparative analysis of some of the other solutions that have been proposed |
proc step, proc fcmp, proc sql | source | 7 |
| 363 | Your Place or Mine: Data-Driven Summary Statistic Precision | Nancy Brucken | The number of decimal places required for displaying summary statistics on a specific parameter is generally a function of the precision of the raw values collected for that parameter and the specific summary statistic requested. This logic can easily be hard-coded for tables displaying only a limited number of parameters, but becomes more difficult to maintain for tables displaying many such parameters. This paper presents a data-driven solution to the problem of displaying summary statistics at the correct level of precision for a larger number of parameters, such as are commonly found on summaries of clinical laboratory data | proc means, proc step, format statement, raw data, proc sql, laboratory data | source | 4 |
| 364 | Your Survival Guide to Using Time-Dependent Covariates | Melissa E. Bagnell, Teresa M. Powell |
Survival analysis is a powerful tool with many strengths, like the ability to handle variables that change over time. Including time-dependent variables in survival analyses models, such as income, marital status, location, or treatment, can more accurately assess the data. This paper will give examples of the counting process syntax and programming statements which are the two methods to apply time- dependent variables in PROC PHREG. Coding techniques will be discussed as well as the pros and cons of both methods |
response variable, predictor variable, time-dependent covariate, binary variable, proc phreg, maximum likelihood estimation, likelihood function, proc step, confidence interval, survival analysis | source | 10 |
| 365 | Zen and the Art of SAS Maintenance: Maintaining and Upgrading a Well-Oiled SAS Deployment | Donna Bennett, Shannon Leslie, Jason Losh, Mark L. Schneider, Jane Stovic |
As a SAS Administrator, you are responsible for managing changes to your deployed SAS environment. You likely have questions about best practices. How can you keep track of the SAS hot fixes and maintenance releases? How often should you apply them? What happens when you run the tools? What best practices will help make sure that changes will not negatively impact your production SAS system? This paper highlights the planning processes and available tools and resources that can help when you are implementing software changes. Making changes to a working SAS deployment is easier when you understand the key components that should be included in your planning cycle, and when you have some insight into the tasks performed by the SAS tools. Learn tips for determining whether a hot fix that you have just read about has been applied to your SAS deployment. Learn more about the difference between uninstalling and removing a configuration | production run, sas administrator | source | 21 |
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