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BUGSSGF 2014 Papers - Index
395 papers. Source: SAS Global Forum conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %COVTEST: A SAS Macro for Hypothesis Testing in Linear Mixed Effects Models via Parametric Bootstrap | Peter Ott | Inference of variance components in linear mixed effect models (LMEs) is not always straightforward. I introduce and describe a flexible SAS macro (%COVTEST) that uses the likelihood ratio test (LRT) to test covariance parameters in LMEs by means of the parametric bootstrap. Users must supply the null and alternative models (as macro strings), and a data set name. The macro calculates the observed LRT statistic and then simulates data under the null model to obtain an empirical p-value. The macro also creates graphs of the distribution of the simulated LRT statistics. The program takes advantage of processing accomplished by PROC MIXED and some SAS/IML functions. I demonstrate the syntax and mechanics of the macro using three examples |
proc step, proc mixed, bootstrap, proc glimmix, covariance, null hypothesis, restricted maximum likelihood, likelihood function, chi-square distribution, main effect | source | 9 |
| 2 | A Case Study: Performance Analysis and Optimization of SAS Grid Computing Scaling on a Shared Storage | Suleyman Sair, Brett Lee, Ying M. Zhang |
SAS Grid Computing is a scale-out SAS solution that enables SAS applications to better utilize I/O and compute intensive computing resources. This requires the use of high-performance shared storage (SS) that allows all servers to access the same file system. SS may be implemented via traditional NFS NAS or clustered file systems (CFS) like GPFS. This paper uses the Intel Enterprise Edition for Lustre1 (IEEL) file system, a parallel, distributed CFS, for a case study of performance scalability of SAS Grid Computing nodes on SS. The paper qualifies the performance of a standardized SAS workload running on IEEL at scale. Lustre is traditionally used for large block sequential I/O. We present the tuning changes necessary for the optimization of IEEL for SAS applications. In addition, results from the scaling of SAS Cluster jobs running on IEEL are presented | fact table, grid computing, sas grid | source | 8 |
| 3 | A Framework Based on SAS for ETL and Reporting | Kevin Chung | Nowadays, most corporations build and maintain their own data warehouse and an ETL (Extract, Transform, and Load) process plays a critical role in managing the data. Some people might create a large program and execute this program from top to botto m. Others might generate a SAS driver with several programs included and execute this driver. If some programs can be run in parallel, then devel opers must write extra codes to handle these concurrent processes. If one program fails then users can either rerun the entire pr ocess or comment out the successful programs and resume the job from where the program failed . Usually the programs are deployed in production with read and execute permission only. Users do not have the privilege of modifying codes on the fly. In this case, how do you comment out the programs if job terminated abnormally? This paper illustrates an approach for managing ETL PROC ess flows. The approach uses a framework based on SAS, on a UNIX platform |
proc sql, macro variable, error message, sample, data step, dataset, macro loop, macro definition | source | 20 |
| 4 | A Guide to SAS for the IT Organization | Lisa Ann Horwitz | SID file, SAS Deployment Wizard, SAS Migration Utility, SAS Environment Manager, plan file. SAS can seem very mysterious to IT organizations used to working with other software solutions. The more IT knows and understands about SAS-how it works, what its system requirements are, how to maintain it and back it up, and what its value is to the organization-the better IT can support the SAS shop. This paper provides an introduction to the world of SAS and sheds light on some of the unique elements of maintaining a SAS environment | sas management console, sas administrator, data mining | source | 5 |
| 5 | A Mental Health and Risk Behavior Analysis of American Youth Using PROC FACTOR and SURVEYLOGISTIC | Deanna Naomi Schreiber-Gregory | The current study looks at recent health trends and behavior analyses of youth in A merica. Data used in this analysis was provided by the Center for Disease Control and Prevention and gathered using the Youth Risk Behavior Surveillance System (YRBSS). A factor analysis w as performed to identify and define latent mental health a nd risk behavior variables. A series of logistic regression analyses w ere then performed using the ris k behavior and demographic variables as potential contributing factors to each of the mental health variables. Mental health variables included disordered eating and depression/suicidal ideation data w hile the risk behavior variables included s moking, consumption of alcohol and drugs, violence, vehicle safety, and sexual behavior data. Implications derived from the results of this research are a primary foc us of this study. Risks and benefits of using a factor analysis w ith logistic regression in social science research w ill also be discussed in depth | likelihood function, maximum likelihood estimation, odds ratio, regression analysis, factor analysis, logistic regression, binary variable, logistic distribution, response variable, factor loading, proc step, correlation, proc freq, sample, proc means | source | 23 |
| 6 | A Note on Type Conversions and Numeric Precision in SAS: Numeric to Character and Back Again | Andrew W. Clapson | One of the first lessons that SAS programmers learn on the job is that numeric and character variables do not play well together, and that type mismatches are one of the more common source of errors in their-otherwise flawless- SAS programs. Luckily, converting variables from one type to another in SAS (that is, casting) is not difficult, requiring only the judicious use of either the input() or put() function. There remains, however, the danger of data being lost in the conversion process. This type of error is most likely to occur in cases of character-to-numeric variable conversion, most especially when the user does not fully understand the data contained in the data set. This paper will review the basics of data storage for character and numeric variables in SAS, the use of formats and informats for conversions, and how to ensure accurate type conversion of even high-precision numeric values | numeric variable, character variable, input statement, put statement, length statement | source | 4 |
| 7 | A Paradigm Shift: Complex Data Manipulations with DS2 and In-Memory Data Structures | Shaun Kaufmann | Complex data manipulations can be resource intensive, both in terms of development time and processing duration. However in recent years SAS has introduced a number of new technologies that when used together can produce a dramatic increase in performance while simultaneously simplifying program development and maintenance. This paper presents a development paradigm that utilizes the problem decomposition capabilities of DS2, the flexibility of SQL and the performance benefits of in-memory processing using hash objects | hash table, proc sql, data warehouse, proc step, in-memory analytics, extract, raw data, sql pass-through, sysrc, sas base | source | 10 |
| 8 | A Poor/Rich SAS User's PROC EXPORT | Arthur S. Tabachneck, Tom Abernathy, Matthew J. Kastin |
Have you ever wished that with one click you could copy any SAS dataset, including variable names, so that you could paste the text into a Word file, powerpoint or spreadsheet? You can and, with just base SAS, there are some little known but easy to use methods that are available for automating many of your (or your users') common tasks | excel, xls, proc export, proc step, dataset, data step, macro loop, %if-%then-%else | source | 10 |
| 9 | A Risk Score Calculator for Short-Term Morbidity Following Hip Fracture Surgery | Yubo Gao | Hip fractures are a common source of morbidity and mortality amongst the elderly. While multiple prior studies have identified risk factors for poor outcomes, few studies have presented a validated method for stratifying patient risk. The purpose of this study was to develop a simple risk score calculator tool predictive of 30-day morbidity after hip fracture. To achieve this, we queried a database prospectively maintained by The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) to identify all cases of hip fracture between 2005 and 2010, based on primary Current Procedural Terminology (CPT) codes. Patient demographics, comorbidities, laboratory values, and operative characteristics were compared in a univariate analysis, and a multivariate logistic regression analysis was then used to identify independent predictors of 30 day morbidity | insurance risk score, logistic regression, regression analysis, logistic distribution, sample, data cleaning | source | 5 |
| 10 | A SAS Macro for Complex Sample Data Analysis Using Generalized Linear Models | Paulo Henrique Dourado da Silva, Alan Ricardo da Silva |
The purpose of this paper is showing a SAS macro named %surveyglm developed in IML Procedure in order that users can incorporate informations about s urvey design to the Generalized Linear Models (GLM). The R function svyglm (Lumley, 2004) was used as background to the %surveyglm macro estimates. The results showed that estimates are close to the R function and that new distributions can be easily added to the algorithm |
macro loop, binomial distribution, sample, proc calis, response variable, likelihood function, odds ratio, proc print, proc step, estimate, proc genmod, macro conditional, logistic distribution, proc logistic, proc model, negative binomial regression, regression analysis, confidence interval, macro call, maximum likelihood estimation, proc surveyreg, poisson distribution, sample weight | source | 19 |
| 11 | A SAS Macro to Diagnose Influential Subjects in Longitudinal Studies | Grant Schneider, Randy Tobias |
Influence analysis in statistical modeling looks for observations that unduly influence the fitted model. Cook's distance is a standard tool for influence analysis in regression. It works by measuring the difference in the fitted parameters as individual observations are deleted. You can apply the same idea to examining influence of groups of observations- for example, the multiple observations for subjects in longitudinal or clustered data-but you need to adapt it to the fact that different subjects can have different numbers of observations. Such an adaptation is discussed by Zhu, Ibrahim, and Cho (2012), who generalize the subject size factor as the so-called degree of perturbation, and correspondingly generalize Cook's distances as the scaled Cook's distance. This paper presents the %SCDMixed SAS macro, which implements these ideas for analyzing influence in mixed models for longitudinal or clustered data | outlier, cook's distance, proc mixed, bootstrap, covariance, repeated measures, sample, standard deviation, macro definition, longitudinal data, proc step, monte carlo, response variable | source zip |
18 |
| 12 | A Stepwise Algorithm for Generalized Linear Mixed Models | Nagaraj Neerchal, Jorge Morel, Xuang Huang, Alain Moluh |
Stepwise regression includes regression models in which the predictive variables are selected by an automated algorithm. The stepwise method involves two approaches, namely, backward elimination and forward selection. Currently, SAS has several regression procedures capable of performing stepwise regression. Among them are REG, LOGISTIC, GLMSELECT and PHREG. PROC REG handles linear regression model but does not support a CLASS statement. PROC LOGISTIC handles binary responses and allows for logit, probit and complementary log-log link functions. It also allows for CLASS statements. The GLMSELECT procedure performs selections in the framework of general linear models. It allows for a variety of model selection methods, including the LASSO method of Tibshirani (1996) and the related LAR method of Efron et al. (2004). GLMSELECT supports a CLASS statement. PHREG is appropriate for proportional hazard survival regression |
regression analysis, feature selection, response variable, stepwise selection, proc step, main effect, proc glimmix, predictor variable, interaction effect, logistic distribution, fixed effects, proc mixed, class variable, covariance, proc reg, poisson distribution | source | 11 |
| 13 | 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 |
| 14 | A Way to Fetch User Reviews from iTunes Using SAS | Jiawen Liu, Mantosh Kumar Sarkar, Meizi Jin, Goutam Chakraborty |
Presentation | data cleaning, dataset | source | 3 |
| 15 | Absolute_Pixel_Width? Taming Column Widths in the ExcelXP Tagset | Dylan Ellis | Presentation | excel, proc step, proc print, proc sql | source | 4 |
| 16 | Accelerated Tests as an Effective Means of Quality Improvement | Bobby Gutierrez | Accelerated testing is an effective tool for predicting when systems fail, where the system can be as simple as an engine gasket or as complex as a magnetic resonance imaging (MRI) scanner. In particular, you might conduct an experiment to determine how factors such as temperature and voltage impose enough stress on the system to cause failure. Because system components usually meet nominal quality standards, it can take a long time to obtain failure data under normal-use conditions. An effective strategy is to accelerate the experiment by testing under abnormally stressful conditions, such as higher temperatures. Following that approach, you obtain data more quickly, and you can then analyze the data by using the RELIABILITY procedure in SAS/QC . The analysis is a three-step process: you establish a probability model, explore the relationship between stress and failure, and then extrapolate to normal-use conditions |
accelerated failure time, estimate | source | 9 |
| 17 | Adding Serial Numbers to SQL Data | Howard L. Schreier | Structured Query Language (SQL) does not recognize the concept of row order. Instead, query results are thought of as unordered sets of rows. Most workarounds involve including serial numbers, which can then be compared or subtracted. This presentation illustrates and compares five techniques for creating serial numbers | proc sql, proc step, file statement | source | 14 |
| 18 | Adding the Power of DataFlux to SAS Programs Using the DQMATCH Function | Pat Taylor, Lee Branum-Martin |
The SAS Data Quality Server allows SAS programmers to integrate the power of DataFlux into their data cleaning programs. The power of SAS Data Quality Server enables programmers to efficiently identify matching records across different datasets when exact matches are not present. During a recent educational research project, the DQMATCH function proved very capable when trying to link records from disparate data sources. Two key insights led to even greater success in linking records. The first insight was acknowledging that the hierarchical structure of data can greatly improve success in matching records. The second insight was that the names of individuals can be restructured to improve the chances of successful matches. This paper provides an overview of how these insights were implemented using the DQMATCH function to link educational data from multiple sources | data step, data cleaning | source | 6 |
| 19 | Adjusting Clustering: Minimize Your Suffering! | Brenda Beaty, L. Miriam Dickinson |
The randomized controlled trial is the 'gold standard' in experimental design. In theory, assigning participants randomly to the experimental groups (intervention and control) make it more likely that any effect is due to the intervention rather than underlying characteristics of the participants. Cluster- or group- randomized trials (CRTs) are seeing increased use. There are many reasons for this, including more pragmatic trials associated with comparative effectiveness research. Examples of clusters that could be randomized for study are clinics or hospitals, counties within a state, and other geographical areas such as communities. In many of these trials, the number of clusters is relatively small. This fact complicates randomization, since a random grouping could still randomize clusters that may have very similar characteristics to one group. In order for the randomization to be most efficient, we would have baseline characteristic data about the clusters | randomization, proc step | source | 9 |
| 20 | Advanced Mobile Reporting with the ODS EPUB3 Destination | David W. Kelley | The Base SAS 9.4 Output Delivery System (ODS) EPUB destination enables users to deliver SAS reports as e- books on Apple mobile devices. The first maintenance release of SAS 9.4 adds the ODS EPUB3 destination, which offers powerful new multimedia and presentation features to report writers. This paper shows you how to include images, audio, and video in your ODS EPUB3 e-book reports. You learn how to use publishing presentation techniques such as sidebars and multicolumn layouts. You become familiar with best practices for accessibility when employing these new features in your reports. This paper provides advanced instruction for writing e-books with ODS EPUB. Please bring your iPad, iPhone, or iPod to the presentation so that you can download and read the examples |
proc step, sas base, output delivery system | source | 19 |
| 21 | Advanced Security Configuration Options for SAS 9.4 Web Applications and Mobile Devices | Heesun Park | SAS 9.4 has overhauled web authentication schemes, and the integration with enterprise security infrastructure is quite different from that of SAS 9.3. This paper examines advanced security features such as Secure Sockets Layer (SSL) configuration, single sign-on (SSO) support through Integrated Windows authentication (IWA), and third-party security packages like CA SiteMinder and IBM Tivoli Access Manager WebSEAL. FIPS 140-2 compliance efforts that enforce the use of a stronger encryption algorithm for web communication and the SAS system itself are also described. The authentication support for mobile devices such as the iPad is different. The secure Wi-Fi connection from a mobile device to the IT internal resources, as well as how it can be safely integrated into the enterprise security configuration by using the same user repository as the SAS web applications, is explained. The configuration example is shown with SAS Visual Analytics 6.3 | data cleaning, business intelligence | source | 8 |
| 22 | An Advanced Fallback Authentication Framework for SAS 9.4 and SAS Visual Analytics | Zhiyong Li, Mike Roda |
SAS 9.4 and SAS Visual Analytics (VA) support a wide list of authentication protocols such as Integrated Windows Authentication (IWA), client certificate, IBM WebSEAL, CA SiteMinder and SAML 2.0. However, advanced customers may want to be able to use some of these protocols together and also have the flexibility to select which protocols to use. In this paper, we will focus on a "fallback" authentication framework that supports IWA as the primary authentication method. When IWA fails, it uses the X509 client certificate as the secondary authentication method, and when the client certificate fails, it uses the form-based username/password as the last option. The paper will first introduce the security architecture of SAS 9.4 and SAS VA. It will then review the three mentioned security protocols. Further, it will introduce the detailed "fallback" authentication framework and discuss how to configure it | cas, sas visual analytics, service provider, data pipeline | source | 15 |
| 23 | An Analytical Approach for Bot Cheating Detection in a Massive Multiplayer Online Racing Game | Andrea Villanes | Presentation | proc hpsplit | source | 10 |
| 24 | An Ensemble Approach for Integrating Intuition and Models | Masoud Charkhabi, Ling Zhu |
Presentation | cluster analysis, feature selection | source | 3 |
| 25 | An Implementation of MapReduce in Base SAS | David Moors | This paper will explain how a simple processing framework created by Google, and more recently popularised by the Open Source technology Hadoop, can be replicated using cornerstone SAS technologies such as BASE/SAS, SAS/MACRO and SAS/CONNECT. The paper will explain how, out-of-the-box, the SAS DATASTEP can replicate the 'Map' function, and we'll discover how well established SAS Procedures can be used to create 'Reducer' like functionality. We'll also see how Parallel Processing data across multiple SAS Sessions using MP/CONNECT can replicate MapReduce's approach to data processing. MAPREDUCE DEFINITION MapReduce is a programming model for processing parallelisable jobs across large dataset using a large number of nodes (computers). The concept was made popular, and later patented, by Google, and is based on the MAP and REDUCE functions found in the functional programming language LISP. Key-value pairs form the basic structure for MapReduce tasks |
macro variable, parallel processing, proc sql, data wrangling, proc step, batch processing, filename statement, view, temporary dataset, dataset | source | 16 |
| 26 | An Insider's Guide to SAS/ACCESS Interface to ODBC | Jeffrey Bailey | The SAS/ACCESS Interface to ODBC has been around forever. On one hand, ODBC is very easy to use. That ease hides the flexibility that ODBC offers. This paper shows you how to increase your program's performance and troubleshoot problems. You will learn the differences between ODBC and OLE DB, what the odbc.ini file is (and why it is important), and how to discover what your ODBC driver is actually doing. In the past you have scratched your head and wondered, "What is the difference between a native ACCESS engine and SAS/ACCESS Interface to ODBC?" This paper answers that question |
proc sql, libname statement, google bigquery, performance tuning, relational database, sql server, odbc connection, sas enterprise guide, macro put | source | 12 |
| 27 | An Intermediate Primer to Estimating Linear Multilevel Models Using SAS PROC MIXED | Bethany A. Bell, Whitney Smiley, Mihaela Ene, Genine L. Blue |
This paper expands upon Bell et al.'s (2013) "A Multilevel Model Primer Using SAS PROC MIXED" in which we presented an overview of estimating two and three-level linear models via PROC MIXED. In our earlier paper, we mainly relied on simple options available in PROC MIXED. In this paper, however, we present a more advanced look at common PROC MIXED options used in the analysis of social and behavioral science data, as well as introduce users to two different SAS macros previously developed for use with PROC MIXED: one to examine model fit (MIXED_FIT; Ene, Smiley, & Bell, 2013) and the other to examine distributional assumptions (MIXED_DX; Bell et al., 2010) |
proc mixed, covariance, degrees of freedom, proc step, random slope, fixed effects, proc calis, sample, restricted maximum likelihood, covariance structure, estimate, likelihood function, sample size, mahalanobis distance, histogram, maximum likelihood estimation, sas syntax, outlier, proc means, correlation, random intercept, repeated measures, unstructured covariance, macro definition | source | 19 |
| 28 | An Introduction to Bayesian Analysis with SAS/STAT Software | Maura E. Stokes, Fang K. Chen, Funda Güneş |
The use of Bayesian methods has become increasingly popular in modern statistical analysis, with applica- tions in numerous scientific fields. In recent releases, SAS has provided a wealth of tools for Bayesian analysis, with convenient access through several popular procedures as well as the MCMC procedure, which is designed for general Bayesian modeling. This paper introduces the principles of Bayesian inference and reviews the steps in a Bayesian analysis. It then describes the built-in Bayesian capabilities provided in SAS/STAT, which became available for all platforms with SAS/STAT 9.3, with examples from the GENMOD and PHREG procedures. How to specify prior distributions, evaluate convergence diagnostics, and interpret the posterior summary statistics is discussed. Foundations Bayesian methods have become a staple for the practicing statistician |
bayesian statistics | source | 25 |
| 29 | An Introduction to SAS Studio | Michael Monaco, Marie Dexter, Jennifer Tamburro |
This paper is an introduction to SAS Studio and covers how to perform basic programming tasks in SAS Studio. Many people program in the SAS language by using SAS Display Manager or SAS Enterprise Guide. SAS Studio is different because it enables you to write and run SAS code by using the most popular web browsers, without requiring a SAS 9.4 installation on your machine. With SAS Studio, you can access your data files, libraries, and existing programs, and write new programs while using SAS software behind the scenes. SAS Studio connects to a SAS server in order to process SAS programs. The SAS server can be a hosted server in a cloud environment, a server in your local environment, or a copy of SAS on your local machine | sas studio, sas enterprise guide, dataset | source | 22 |
| 30 | An Investigation of the Kolmogorov-Smirnov Nonparametric Test Using SAS | Tison Bolen, Dawit Mulugeta, Jason Greenfield, Lisa Conley |
The Kolmogorov-Smirnov (K-S) test is one of the most useful and general nonparametric methods for comparing two samples. It is sensitive to all types of differences between two populations (shift, shape, etc.). In this paper, we will present a thorough investigation into the K-S test including: discussion of the formal test procedure, practical demonstration of the test, large sample approximation of the test statistic and ease of use in SAS using the NPAR1WAY procedure |
sample, normality test, probability distribution, cumulative frequency, proc npar1way, nonparametric test, proc step | source | 8 |
| 31 | An Overview of Machine Learning with SAS Enterprise Miner | Patrick Hall, Jared Dean, Ilknur Kaynar Kabul, Jorge Silva |
SAS and SAS Enterprise MinerTM have provided advanced data mining and machine learning capabilities for years-beginning long before the current buzz. Moreover, SAS has continually incorporated advances in machine learning research into its classification, prediction, and segmentation procedures. SAS Enterprise Miner now includes many proven machine learning algorithms in its high- performance environment and is introducing new leading-edge scalable technologies. This paper provides an overview of machine learning and presents several supervised and unsupervised machine learning examples that use SAS Enterprise Miner. So, come back to the future to see machine learning in action with SAS! | machine learning, data mining, supervised learning, neural network, proc step, semi-supervised learning, training set, sas base, proc princomp, data science, unsupervised learning, sample, data cleaning, deep learning, feature extraction, customer segmentation, monte carlo, score code, validation set, feature selection, proc hpforest | source zip |
24 |
| 32 | Analysis of Data with Overdispersion Using SAS | Jorge Morel | Presentation | binomial distribution, poisson distribution, likelihood function, type i error, zero-inflated poisson, proc step, significance level | source | 67 |
| 33 | Analysis of Unstructured Data: Applications of Text Analytics and Sentiment Mining | Goutam Chakraborty, Murali Krishna Pagolu |
The proliferation of textual data in business is overwhelming. Unstructured textual data is being constantly generated via call center logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, and so on. While the amount of textual data is increasing rapidly, businesses' ability to summarize, understand, and make sense of such data for making better business decisions remain challenging. This paper takes a quick look at how to organize and analyze textual data for extracting insightful customer intelligence from a large collection of documents and for using such information to improve business operations and performance. Multiple business applications of case studies using real data that demonstrate applications of text analytics and sentiment mining using SAS Text Miner and SAS Sentiment Analysis Studio are presented. While SAS products are used as tools for demonstration only, the topics and theories covered are generic (not tool specific) | unstructured data, root cause analysis | source | 14 |
| 34 | Analyzing Data from Experiments in Which the Treatment Groups Have Different Hierarchical Structures | Sharon Lohr, Peter Schochet |
In randomized experiments, it is generally assumed that the hierarchical structures and variances are the same in the treatment and control groups. In some situations, however, these structures and variance components can differ. Consider a randomized experiment in which individuals randomized to the treatment condition are further assigned to clusters in which the intervention is administered, but no such clustering occurs in the control condition. Such a structure can occur, for example, when the individuals in the treatment condition are randomly assigned to group therapy sessions or to mathematics tutoring groups: individuals in the control condition do not receive group therapy or mathematics tutoring and therefore do not have that level of clustering. In this example, individuals in the treatment condition have a hierarchical structure but individuals in the control condition do not | treatment effect, proc step, proc mixed, degrees of freedom, proc glimmix, interval variable, randomization, covariance, regression analysis, likelihood function, nominal variable, proc varclus | source | 12 |
| 35 | Analyzing Interval-Censored Data with the ICLIFETEST Procedure | Changbin Guo, Ying C. So, Gordon Johnston |
SAS/STAT 13.1 includes the new ICLIFETEST procedure, which is specifically designed for analyzing interval-censored data. This type of data is frequently found in studies where the event time of interest is known to have occurred not at a specific time but only within a certain time period. PROC ICLIFETEST performs nonparametric survival analysis of interval-censored data and is a counterpart to PROC LIFETEST, which handles right-censored data. With similar syntax, you use PROC ICLIFETEST to estimate the survival function and to compare the survival functions of different populations. This paper introduces you to the ICLIFETEST procedure and presents examples that illustrate how you can use it to perform analyses of interval-censored data |
proc step, censoring, survival function, rank test, bootstrap, log-rank test, likelihood function | source | 16 |
| 36 | Analyzing Multilevel Models with the GLIMMIX Procedure | Min Zhu | Hierarchical data are common in many fields, from pharmaceuticals to agriculture to sociology. As data sizes and sources grow, information is likely to be observed on nested units at multiple levels, calling for the multilevel modeling approach. This paper describes how to use the GLIMMIX procedure in SAS/STAT to analyze hierarchical data that have a wide variety of distributions. Examples are included to illustrate the flexibility that PROC GLIMMIX offers for modeling within-unit correlation, disentangling explanatory variables at different levels, and handling unbalanced data. Also discussed are enhanced weighting options, new in SAS/STAT 13.1, for both the MODEL and RANDOM statements. These weighting options enable PROC GLIMMIX to handle weights at different levels. PROC GLIMMIX uses a pseudolikelihood approach to estimate parameters, and it computes robust standard error estimators |
proc mixed, covariance, proc glimmix, sample, random intercept, proc step, estimate, likelihood function, proc calis | source | 18 |
| 37 | Analyzing U.S. Healthcare Cost and Use with SAS | Paul Gorrell | A central component of discussions of healthcare reform in the U.S. are estimations of healthcare costs at the national level, as well as for subpopulation analyses for individuals with certain demographic properties or medical conditions. For example, a striking but persistent observation is that just 1% of the U.S. population accounts for more than 20% of total healthca re costs, and 5% account for almost 50% of total costs. In addition to descriptions of specific data sources underlying this type of observation, we demonstrate how to use SAS to generate these estimates and to extend the analysis in various ways; that is , to investigate costs for specific subpopulations. The goal is to provide SAS programmers and healthcare analysts with sufficient data -source background and analytic resources to independently conduct analyses on a wide variety of topics in healthcare res earch | dataset, proc step, proc surveymeans, proc freq, sample, proc calis, binary variable, proc surveyfreq, numeric variable, standard deviation, health insurance, sas programming | source | 17 |
| 38 | Answer Frequently Asked SAS Usage Questions with the Help of RTRACE | Airaha Chelvakkanthan Manickam | SAS license of any organization consists of a variety of SAS components such as SAS/STAT, SAS/GRAPH, SAS/OR, etc. SAS Administrator do not have any automated tool supplied with Base SAS software to find how many of licensed copies are being actively used, how many SAS users are actively utilizing the SAS server and how many SAS datasets are being referenced. These questions help a SAS administrator to take important decisions such as controlling SAS licenses, removing inactive SAS users, purging long-time non-referenced SAS datasets, etc. SAS provides a system parameter called RTRACE to answer these questions. The goal of this paper is to explain the set-up of RTRACE parameter and to explain the usage of it in making the SAS administrator's life easy. This paper is based on SAS 9.2 running on AIX operating system |
dataset, sas enterprise guide, options statement, sample, sas administrator | source | 12 |
| 39 | Application of Survey Sampling for Quality Control | Yi Du | Sampling is widely used in different fields for quality control, population monitoring, and modeling. However, the purposes of sampling might be justified by the business scenario, such as legal or compliance needs. This paper uses one probability sampling method, stratified sampling, combined with quality control review business cost to determine an optimized procedure of sampling that satisfies both statistical selection criteria and business needs. The first step is to determine the total number of strata by grouping the strata with a small number of sample units, using box-and-whisker plots outliers as a whole. Then, the cost to review the sample in each stratum is justified by a corresponding business counter-party, which is the human working hour. Lastly, using the determined number of strata and sample review cost, optimal allocation of predetermined total sample population is applied to allocate the sample into different strata | sample, outlier, sample size, stratified sampling, quality control, macro definition, box plot, interquartile range, macro variable, random sampling, proc step | source | 8 |
| 40 | Application of Text Mining in Tweets Using SAS and R, and Analysis of Change in Sentiments toward Xbox One Using SAS Sentiment Analysis Studio | Aditya Datla, Reshma Palangat, Goutam Chakraborty |
The power of social media has increased to such an extent responses on social networking sites are now clearly at a disadvantage. In this paper insights on the impact of the Digital Rights Management (DRM) policies of Microsoft and the release of Xbox One on their customers' reactions. We have conducted preliminary research to compare the basic text mining capabilities of SAS and R, two very diverse yet powerful tools. Our results suggest that to extensive analysis of textual data. Furthermore, we performed sentiment analysis on after the launch of Xbox One and found a positive valence in the opinions. Probing further into the sentiments (using Feature based analysis) of customers pertaining to the controller, we see a negative response perhaps attributed to the comfort level in comparison with the PS4 contr | source | 8 | |
| 41 | Assessing the Impact of Factors in a Facebook Post that Influence the EdgeRank Metric of Facebook Using the Power Ratio | Dinesh Yadav Gaddam, Yogananda Domlur Seetharama |
Presentation | source | 3 | |
| 42 | Assigning Agents to Districts Under Multiple Constraints Using PROC CLP | Kevin K. Gillette, Stephen B. Sloan |
The Challenge: assigning outbound calling agents in a telemarketing campaign to geographic districts. The districts have a variable number of leads and each agent needs to be assigned entire districts with the total number of leads being as close as possible to a specified number for each of the agents (usually, but not always, an equal number). In addition, there are constraints concerning the distribution of assigned districts across time zones, in order to maximize productivity and availability. Our Solution: uses the SAS/OR procedure PROC CLP to formulate the challenge as a constraint satisfaction problem (CSP), since the objective is not necessarily to minimize a cost function, but rather to find a feasible solution to the constraint set |
proc step, macro variable | source | 10 |
| 43 | Association Mining of Brain Data: An EEG Study | Pankush Kalgotra, Ramesh Sharda, Goutam Chakraborty |
Many neuroscience researchers have explored how various parts of the brain are connected. No one has performed association mining using the brain data. In this study, we used SAS Enterprise MinerTM 7.1 for association mining of the brain data collected by a 14-channel EEG device. An application of the association mining technique is presented in this novel context of brain activities and by linking our results to theories of cognitive neuroscience. The brain waves are collected while a user processes the information about Facebook, the most well known social networking site. The data is cleaned using Independent Component Analysis via an open source MATLAB package. Next, by applying the LORETA algorithm, activations at a fraction of a second are recorded. The data is codified into transactions to perform association mining. Results showing how various parts of brain that get excited while processing the information are reported | association rules, data mining | source | 6 |
| 44 | Association of Socio-Emotional Factors: Religious Affiliation, Depression to Suicidal Tendency Among Adolescent Girls | Sesha Sai Ega, Chandra Shekar Pulipati, Venkata Rachapudi |
Suicidal tendency among adolescent girls is a big challenge for the present day society. The main goal of this paper is to find associations of the various socio-emotional factors to suicidal tendencies among adolescent girls in the United States. The data were obtained from the National Longitudinal Study of Adolescent Health, to explore social behavior among adolescents. The observations from "National Longitudinal Study of Adolescent Health" represent a nationally representative sample of adolescents in grades 7 through 12 in the US. Students in each school were stratified by grade and sex. About 17 students were randomly chosen from each stratum so that a total of approximately 200 adolescents were selected from each of the 80 pairs of schools. The public access sample includes 6,504 adolescents. Models built via multiple regressions are used to find the association between depression, religious affiliation and suicidal tendency | regression analysis, logistic regression, proc reg, post hoc test, sample, maximum likelihood estimation, logistic distribution, likelihood function, interval variable, predictor variable, anova, response variable | source | 6 |
| 45 | Auditing an Enterprise SAS Visual Analytics 6.2 Environment with SAS Tools: From the SAS IT Perspective | Dan Lucas, Brandon Kirk |
With a growing enterprise analytics environment comprised of global users and a variety of sensitive data sources, a system administrator is faced with several challenges. These include knowing who logs in to the system, how often, and what applications and data sources are being consumed. This information is necessary for auditing the consumers of data as well as monitoring the growth of data sources for hardware expansion. With the usage of the SAS Audit, Performance and Measurement Package, along with some additional middle-tier logging and SAS code, information about the major consumers of the environment can be loaded into LASR tables and analyzed with SAS Visual Analytics reporting tools | bar chart, business intelligence, memory usage, proc sql | source | 28 |
| 46 | Automatic and Efficient Post-Campaign Analyses By Using SAS Macro Programs | Justin Jia, Amanda Lin |
In the past, we often needed to perform huge numbers of repetitive and data-driven statistical association analyses to evaluate the performance of business events in terms of the event outcome or target response. These routine tasks were usually carried out manually by using Microsoft EXCEL, which was tedious, time-consuming and error-prone in nature. In order to improve the work efficiency and analysis accuracy, we managed to automate the post-event analysis process by SAS programming to replace the manual work with EXCEL. Through the use of SAS MACRO and other advanced skills, we successfully automated the complicated data-driven analyses with high efficiency and accuracy |
excel, proc freq, proc step, chi-square test, raw data, macro definition, clinical trial, user-defined format, significance level, sas programming, macro variable, odds ratio, relative risk, hypothesis testing, proc sql, macro loop, proc append, response variable, dataset | source | 12 |
| 47 | Automatic Detection of Section Membership for SAS Conference Paper Abstract Submissions: A Case Study | Goutam Chakraborty, Murali Krishna Pagolu |
parsing, business intelligence, data mining, data science, txt, data cleaning | source | 13 | |
| 48 | Automatically Convert All Numeric Data Stored As Character to True Numeric Data | Andrea Wainwright-Zimmerman | This is a simple macro that will examine all fields in a SAS dataset that are stored as character data to see if they contain real character data or if they could be converted to numeric. It then performs the conversion and reports in the log the names of any fields that could not be converted. This allows the truly numeric data to be analyzed by PROC MEANS or PROC UNIVARIATE. It makes use of the SAS dictionary tables, the SELECT INTO syntax, and the ANYALPHA function |
macro definition, proc step, macro loop, proc sql, anyalpha, create table, macro variable | source | 8 |
| 49 | Automating the Creation of Complex Microsoft PowerPoint Presentations | Julie VanBuskirk | The creation of production reports for our organization has historically been a labor-intensive process. Each month our team produced around 650 SAS graphs and 30 tables which were then copied and pasted into 16 custom PowerPoint presentations, each between 20 and 30 pages. To reduce the number of manual steps, we converted to using stored processes and the SAS Add-In for Microsoft Office to run SAS stored processes. SAS stored processes generate the graphs and tables while SAS Add-In for Microsoft Office refreshes the document with updated graphs already sized and positioned on the slides just as we need them. With this new process we are realizing the dream of reducing the amount of time spent on a single monthly production process. This paper will discuss the steps to creating a complex PowerPoint presentation that is simply refreshed rather than created new each month | sas enterprise guide, excel, proc step, macro variable | source | 12 |
| 50 | Basic Concepts for Documenting SAS Projects: Documentation Styles for SAS Projects, Programs, and Variables | Peter Timusk | Presentation | variable, sas programming | source | 13 |
| 51 | Basic SAS PROCedures for Producing Quick Results | Kirk Paul Lafler | As IT professionals, saving time is critical. Delivering timely and quality looking reports and information to management, end users, and customers is essential. The SAS System provides numerous "canned" PROCedures for generating quick results to take care of these needs ... and more. Attendees learn how basic SAS PROCedures such as PRINT and FORMS produce detail output; CHART, FREQ, MEANS, PLOT, SQL, and UNIVARIATE summarize and create graphical, tabular, and statistical output; and utility PROCedures to manage data libraries; Additional topics include techniques for informing the SAS System which data set to use as input to a procedure, how to subset data using a WHERE statement (or WHERE= data set option), and how to perform BY- group processing | proc step, proc means, numeric variable, libname statement, proc freq, proc sort, proc univariate | source | 18 |
| 52 | Bayesian Framework in Early Phase Drug Development with SAS Examples | Howard Liang | There is an ever-increasing number of study designs and analyses of clinical trials using Bayesian frameworks to interpret the treatment effects. Many research scientists prefer to understand the power and probability of taking a new drug forward across the whole range of possible true treatment effects, rather than focusing on one particular value to power the study. Examples will be used in this paper to show how to compute Bayesian probabilities using the SAS/STAT procedures PROC MIXED and PROC UNIVARIATE. Particular emphasis is given to the application on efficacy analysis, including the comparison of new drug to placebo and to a standard drug on the market |
proc step, proc univariate, proc mixed, clinical trial, clinical research, treatment effect, sample | source | 4 |
| 53 | Before You Get Started: A Macro Language Preview in Three Parts. Part 1: What the Language Is, What It Does, and What It Can Do | Arthur L. Carpenter | Using the macro language adds a layer of complexity to SAS programming that many programmers are reluctant to tackle. The macro language is not intuitive, and some of its syntax and logic runs counter to similar operations in the DATA STEP. This makes the transfer of DATA STEP and PROC STEP knowledge difficult when first learning the macro language. So why should one make the effort to learn a complex counterintuitive language? Before you start to learn the syntax; where to put the semicolon, and how to use the ampersand and percent sign; you need to have a basic understanding of why you want to learn the language in the first place. It will also help if you know a bit about how the language thinks. This overview provides the background that will enable you to understand the way that the macro language operates. This will allow you to avoid some of the common mistakes made by novice macro language programmers |
macro facility, macro variable, proc step, macro definition | source | 9 |
| 54 | Best Practices for Implementing High Availability for SAS 9.4 | Cheryl Doninger, Zhiyong Li, Bryan Wolfe |
There are many components that make up the mid-tier and server-tier of a SAS 9.4 deployment. There is also a variety of technologies that can be used to provide high availability of these components. This paper will focus on a small set of SAS recommended best practices for a consistent high availability strategy across the entire SAS 9.4 platform. We will focus on two technologies: clustering as well as the high availability features of SAS Grid Manager. For the clustering, we will detail newly introduced clustering capabilities in SAS 9.4 such as the middle tier SAS Web Application Server and the server tier SAS metadata clusters. We will also introduce the small, medium and large deployment scenarios or profiles which make use of each of these technologies. These different deployment scenarios will reflect the typical customer's environment and address their high availability, performance and scalability requirements | sas grid, analytics platform | source | 11 |
| 55 | Best Practices in SAS Enterprise Guide | Jennifer First-Kluge, Steven J. First |
This paper provides an overview of how to create a SAS Enterprise Guide process that is well designed, simple, documented, automated, modular, efficient, reliable, and easy to maintain. Topics include how to organize a SAS Enterprise Guide process, how to best document in SAS Enterprise Guide, when to leverage point-and-click functionality, and how to automate and simplify SAS Enterprise Guide processes. This paper has something for any SAS Enterprise Guide user, new or experienced! | sas enterprise guide, sas enterprise guide project | source | 7 |
| 56 | Better Together: Best Practices for Deploying SAS Web Parts for Microsoft SharePoint | Randy Mullis | You can provide access and visibility to SAS BI Dashboards, SAS Stored Processes, and SAS Visual Analytics through the use of SAS Web Parts for Microsoft SharePoint. In many organizations, the administrators who are responsible for SharePoint and SAS are different. Provides best practices for the deployment of the SAS Web Parts. Bridging the gap between SharePoint and SAS is especially important for people who are not familiar with SharePoint administration. This paper also provides tips for co-existence between SAS Web Parts 6.1 and 5.1. (The 5.1 release is available in SAS 9.3. The 6.1 release is available in SAS 9.4.) Finally, this paper provides some guidance on DNS, permissions, and installation techniques -- the fine points that make or break your deployment! | business intelligence, latency | source | 14 |
| 57 | Big Data Analysis for Resource-Constrained Surgical Scheduling | Elizabeth Rowse, Paul Harper |
The scheduling of surgical operations in a hospital is a complex problem, with each surgical specialty having to satisfy their demand while competing for resources with other hospital departments. This project extends the construction of a weekly timetable, the Master Surgery Schedule, which assigns surgical specialties to operating theater sessions by taking into account the post-surgery resource requirements, primarily post-operative beds on hospital wards. Using real data from the largest teaching hospital in Wales, UK, this paper describes how SAS has been used to analyze large data sets to investigate the relationship between the operating theater schedule and the demand for beds on wards in the hospital | outlier, full join | source | 5 |
| 58 | Big Data Everywhere! Easily Loading and Managing Your Data in the SAS LASR Analytic Server | Gary Mehler, Donna Bennett |
SAS Visual Analytics and the SAS LASR Analytic Server provide many capabilities to analyze data fast. Depending on your organization, data can be loaded as a self-service operation. Or your data can be large and shared with many people. As the data gets larger, effectively loading it and keeping it updated become important. This paper discusses a range of data scenarios from self-service spreadsheets to very large databases, from single-subject data to large star schema topologies, and from single-use data to continually updated data that requires high levels of resilience and monitoring. Fast and easy access to big data is important to empower your organization to make better business decisions. Understanding how to have a responsive and reliable data tier on which to make these decisions is within your reach | star schema, dataset, proc sql, data merging, dimension table, excel, in-memory analytics | source | 17 |
| 59 | Big Data/Metadata Governance | Sigurd W. Hermansen | The emerging discipline of data governance encompasses data quality assurance, data access and use policy, security risks and privacy protection, and longitudinal management of an organization's data infrastructure. In the interest of forestalling another bureaucratic solution to data governance issues, this presentation features database programming tools that provide rapid access to big data and make selective access to and restructuring of metadata practical. KEYWORDS Data governance, data quality, data use, security, privacy, SAS, SQL, networks, servers, databases, Big Data, metadata, SAS/ACCESS. Introduction Big Data means some or all of terabytes on storage media, streams of semi-structured data on the Web, blobs of bits in memory, output of sensors, enterprise data infrastructures, federated relational databases, and complex public data releases. Data governance means coordinated management of data quality, access, use, security, privacy protection, updating, and persistence |
data governance, proc sql, big data, data cleaning, missing value, proc step, relational database, dataset | source | 9 |
| 60 | Big Data? Faster Cube Builds? PROC OLAP Can Do It | Yunbo Sun, Michael Brule |
In many organizations the amount of data we deal with increases far faster than the hardware and IT infrastructure to support them. As a result, we encounter significant bottlenecks and I/O bound processes. However, clever use of SAS software can help us find a way around. In this paper we will look at the clever use of PROC OLAP to show you how to address I/O bound processing by spreading I/O traffic across different data mounts to increase cube building and querying efficiency. This paper assumes experience with SAS OLAP Cube Studio and/or PROC OLAP |
proc step, big data | source | 5 |
| 61 | Big Digital Data, Analytic Visualization, and the Opportunity of Digital Intelligence | Suneel Grover | Digital data has manifested into a classic BIG DATA challenge for marketers who want to push past the retroactive analysis limitations of traditional web analytics. The current ground swell of digital device adoption and variety of digital interactions grows larger year after year. The opportunity for "digital intelligence" has arrived, as traditional web analytic techniques were not designed for the breadth of channels, devices, and pace that fuels consumer experiences. In parallel, today's landscape for data visualization, advanced analytics, and our ability to process very large amounts of multi-channel information is changing. The democratization of analytics for the masses is upon us, and marketers have the opportunity to take advantage of descriptive, predictive, and (most importantly) prescriptive data-driven insights | web analytics, customer experience, proc hpsplit, normalization, predictive analytics, business intelligence, data mining, data visualization, data validation rules, data governance, digital analytics, data cleaning | source | 17 |
| 62 | Build your Metadata with PROC CONTENTS and ODS OUTPUT | Louise S. Hadden | Simply using an ODS destination to replay PROC CONTENTS output does not provide the user with attractive, usable metadata. Harness the power of SAS and ODS output objects to create designer multi-tab metadata workbooks with the click of a mouse! |
proc contents, proc step, excel | source | 8 |
| 63 | Building Gamer Segmentation in the Credit Card Industry Using SAS Enterprise Guide | Yang Ge | Credit card companies are competing each other by launching longer and longer 0% balance transfer (BT) period in recent 2 years, which has creating a new segmentation of customer. This is a group of customer who use credit CARDS as an interest -free loan by transferring their balances between c ards during 0% balance transfer periods in order to avoid paying interest. These customers are called gamers. Gamers generate losses for banks due to paying no interest and making no purchases. Since gamers tend to have very good credit histories , it is ineffective to use risk scorecards to identify them according to the definiti on used by existing risk scorecards. This paper uses Naive Bayes classifier to create gamer scores which allow banks to track the proportion of gamers in population . This result has been a chieved using variable selection through logistic regression and dis cretization through interactive grouping . The procedure is described in detail in this paper |
logistic regression, proc hpsplit, confusion matrix, credit card, logistic distribution, regression analysis, forward selection, data cleaning, roc curve, odds ratio, training set, test set, validation set, feature selection, sample | source | 10 |
| 64 | Building Stronger Communities with Integrated Marketing Management | Julie Chalk, Kristine Vick |
Discover how SAS leverages field marketing programs to support AllAnalytics.com, a sponsored third-party community. This paper explores how the use of SAS software (such as SAS Enterprise Guide, SAS Customer Experience Analytics, and SAS Marketing Automation) enables marketers to have more insight, enhanced targeting, and a higher response from SAS marketing programs | customer experience, sas enterprise guide | source | 9 |
| 65 | Calculate All Kappa Statistics in One Step | Matthew Duchnowski | Cohen's kappa statistic has enjoyed a growing popularity in the social sciences as a way of evaluating rater agreement on a common scale. Currently, SAS users can produce the kappa statistic of their choice through PROC FREQ and the use of relevant AGREE options. Complications arise, however, when the data do not possess a completely square cross-tabulation of ratings. That is, this method requires that both raters have at least one data point for every available category on the scale. Many solutions have been offered for this predicament. Most solutions include inserting dummy records into the data and then assigning a weight of zero to those records through an additional class variable. The result is a multistep macro that often creates extraneous observations, variables and datasets that present potential data integrity issues. The author offers a more elegant solution by producing a segment of code that uses brute force to calculate Cohen's kappa as well as all popular variants |
cohen's kappa, proc freq, proc step, group by, proc sql | source | 7 |
| 66 | Case Control Matching: Comparing Simple Distance- and Propensity Score-Based Methods | Lovedeep Gondara, Colleen E. McGahan |
A case control study is basically comparing a case series to a matched control series and is commonly implemented in the field of public health. While matching is intended to eliminate confounding, the main potential benefit of matching in case control studies is a gain in efficiency. There are many known methods for selecting potential match or matches (in case of 1:n studies) per case, the most prominent being distance-based approach and matching on propensity scores. In this paper, we will go through both and compare their results | propensity score, mahalanobis distance | source | 7 |
| 67 | Case-Level Residual Analysis in the CALIS Procedure | Catherine Truxillo | This paper demonstrates the new case-level residuals in the CALIS procedure and how they differ from classic residuals in structural equation modeling (SEM). Residual analysis has a long history in statistical modeling for finding unusual observations in the sample data. However, in SEM, case-level residuals are considerably more difficult to define because of 1) latent variables in the analysis and 2) the multivariate nature of these models. Historically, residual analysis in SEM has been confined to residuals obtained as the difference between the sample and model-implied covariance matrices. Enhancements to the CALIS procedure in SAS/STAT 12.1 enable users to obtain case-level residuals as well. This enables a more complete residual and influence analysis. Several examples showing mean/covariance residuals and case-level residuals are presented |
outlier, robust estimator, covariance, residual, estimate, latent variable, proc step, regression analysis, sample, proc calis, maximum likelihood estimation, confirmatory factor analysis, factor analysis, mahalanobis distance | source | 17 |
| 68 | Challenges of Processing Questionnaire Data from Collection to SDTM to ADaM and Solutions Using SAS | Karin LaPann, Terek J. Peterson |
Often in a clinical trial, measures are needed to describe pain, discomfort, or physical constraints which are visible but not measurable through lab tests or other vital signs. In these cases, researchers turn to questionnaires to provide documentation of improvement or statistically meaningful change in support of safety and efficacy hypotheses. For example, in studies (i.e. Parkinson's) where pain or depression are serious non-motor symptoms of the disease, these questionnaires provide primary endpoints for analysis. Questionnaire data presents unique challenges in both collection and analysis in the world of CDISC standards. The questions are usually aggregated into scale scores, as the underlying questions by themselves provide little additional usefulness. The SAS system is a powerful tool for extraction of the raw data from the collection databases and transposition of columns into a basic data structure in SDTM which is vertical | adam dataset, controlled terminology, raw data, imputation, sample, change from baseline, data science, missing value, output, annotated crf, edc, vital signs, proc step, data cleaning, proc sql, cdisc | source | 19 |
| 69 | Chasing the Log File While Running the SAS Program | Harun Rasheed, Amarnath Vijayarangan |
Prompt error alerts through emails while an error is encountered, will help user to save the run time and restrict the creation of empty datasets from the subsequent codes. It also helps in monitoring the status of the SAS programs submitted without being in front of the computer. While running the program, if SAS finds any error in the current line, it will skip the current statement and will start executing the next statement and the process will be complete only after the execution of the end statement of the program. There are programs which takes more than a day to complete. In such cases, user has to open the log file in read only mode very frequently to check for errors, warnings and unexpected notes. The user will have to terminate the execution of program manually if any such messages are identified else the user will be notified with the errors in the log file only at the end of the execution | error message, macro conditional | source | 6 |
| 70 | Check It Out! Versioning in SAS Enterprise Guide | Joe M. Flynn, Casey Smith, Alex Song |
The life of a SAS program can be broken down into sets of changes made over time. Programmers are generally focused on the future, but when things go wrong, a look into the past can be invaluable. Determining what changes were made, why they were made, and by whom can save both time and headaches. This paper discusses version control and the current options available to SAS Enterprise Guide users. It then highlights the upcoming Program History feature of SAS Enterprise Guide. This feature enables users to easily track changes made to SAS programs. Properly managing the life cycle of your SAS programs will enable you to develop with peace of mind | sas enterprise guide, macro variable, sample | source | 19 |
| 71 | Clustering and Predictive Modeling of Patient Discharge Records with SAS Enterprise Miner | Linda Schumacher, Goutam Chakraborty |
Can clustering improve a predictive model's overall fit statistic? This talk describes the methods and results of data mining pediatric IBD patient records. SAS Enterprise Miner 12.1 was used to segment patients and model important predictors for the length of hospital stay using discharge records from the national Kid's Inpatient Database. Profiling revealed that patient segments were differentiated by primary diagnosis, operating room procedure indicator, comorbidities, and factors related to admission and disposition of patient. Cluster analysis of patient discharges improved the overall average square error of predictive models and distinguished predictors that were unique to patient segments | proc hpsplit, data mining, regression analysis, data cleaning, proc reg, sample, feature importance | source | 9 |
| 72 | CMS Core Measures, the Affordable Care Act, and SAS Visual Analytics | Joe Whitehurst, Diane Hatcher |
confidence interval, standard deviation, proc freq, data science, normal distribution, proc calis, exploratory data analysis, box plot, nominal variable, sample, interval variable, data standard, chi-square test, data visualization, fact table, time series analysis, outlier, histogram, sorted data, proc means | source | 16 | |
| 73 | Collaborative Problem Solving in the SAS Community | Tom Kari | When a SAS user asked for help scanning words in textual data and then matching them to pre-scored keywords, it struck a chord with SAS programmers! They contributed code that solved the problem using hash structures, SQL, informats, arrays, and PRX routines. Of course, the next question was which program is fastest? This paper compares the different approaches and evaluates the performance of the programs on varying amounts of data. The code for each program is provided to show how SAS has a variety of tools available to solve common problems. While this won't make you an expert on any of these programming techniques, you'll see each of them in action on a common problem | sas programmer, problem solving | source | 14 |
| 74 | Combined SAS ODS Graphics Procedures with ODS to Create Graphs of Individual Data | Howard Liang | The graphical display of individual data is important in understanding the raw data and the relationship between the variables in the data set. You can explore your data to ensure statistical assumptions hold by detecting and excluding outliers if they exist. Since you can visualize what actually happens to individual subjects, you can make your conclusions more convincing in statistical analysis and interpretation of the results. SAS provides many tools for creating graphs of individual data. In some cases, multiple tools need to be combined to make the specific type of graph you are after. Examples will be used in this paper to show how to create graphs of individual data using the SAS/SG(Statistical Graphics) procedure with ODS(Output Delivery System) |
bar chart, scatter plot, proc step, outlier, proc sgpanel, proc sgplot, clinical trial, ods pdf | source | 6 |
| 75 | Combining Multiple Date-Ranged Historical Data Sets with Dissimilar Date Ranges into a Single Change History Data Set | James Moon | Presentation | source | 9 | |
| 76 | Combining Type-III Analyses from Multiple Imputations | Binhuan Wang, Yixin Fang, Man Jin |
Missing data commonly occur in medical, psychiatry, and social researches. The SAS procedures MI and MIANALYZE are often used to generate multiple imputations and then provide valid statistical inferences based on them. However, MIANALYZE is not applicable to combine type-III analyses obtained using multiple imputed datasets. In this manuscript, we write a macro to combine the type-III analyses generated from SAS PROCEDURE MIXED based on multiple imputations. The proposed method can be extended to other procedures reporting type-III analyses, such as GENMOD and GLM |
multiple imputation, imputation, degrees of freedom, proc step, proc genmod, proc mixed, proc glm, residual, main effect, anova, missing value, proc mianalyze | source | 6 |
| 77 | Communication-Effective Data Visualization: Design Principles, Widely Usable Graphic Examples, and Code for Visual Data Insights | LeRoy Bessler | Graphic software users are confronted with what I call Options Over-Choice, and with defaults that are designed to easily give you a result, but not necessarily the best result. This presentation and paper focus on guidelines for communication-effective data visualization. It demonstrates their practical implementation, using graphic examples likely to be adaptable to your own work. Code is provided for the examples. Audience members will receive the complete latest update of my tip sheet compendium of graphic design principles. The examples use SAS tools (traditional SAS/GRAPH or the newer ODS graphics procedures that are available with Base SAS), but the design principles are really software independent. Come learn how to use data visualization to inform and influence, to reveal and persuade, using tips and techniques developed and refined over 34 years of working to get the best out of SAS graphic software tools |
bar chart, line chart, time series analysis, horizontal bar chart, series plot, proc step, proc sgplot | source | 38 |
| 78 | Comparison of Five Analytic Techniques for Two-Group, Pre-Post Repeated Measures Designs Using SAS | J. Madison Hyer, Jennifer L. Waller |
There has been debate regarding which method to use to analyze repeated measures continuous data when the design includes only two measurement times. Five different techniques can be applied and give similar results when there is little to no correlation between pre- and post-test measurements and when data at each time point are complete: 1) analysis of variance on the difference between pre- and post-test, 2) analysis of covariance on the differences between pre- and post-test controlling for pre-test, 3) analysis of covariance on post-test controlling for pre-test, 4) multiple analysis of variance on post-test and pre-test, and 5) repeated measures analysis of variance. However, when there is missing data or if a moderate to high correlation between pre- and post-test measures exists under an intent-to-treat analysis framework, bias is introduced in the tests for the ANOVA, ANCOVA, and MANOVA techniques | anova, repeated measures, correlation, ancova, proc step, proc glm, missing value, repeated measures anova, response variable, covariance, proc genmod, proc mixed, group by | source | 8 |
| 79 | Conditional Execution "Switch Path" Logic in SAS Data Integration Studio 4.6 | Prajwal Shetty | With the growth in size and complexity of organizations investing in SAS platform technologies, the size and complexity of ETL subsystems and data integration jobs is growing at a rapid rate. As always data integration developers are pushed to come up with new and innovative ways to improve process efficiency in their ETL design to meet the increasingly demanding SLA's. The ability or the feature to conditionally execute a code branch or switch code execution paths based on the runtime parameters in a SAS Data Integration Studio job is an extremely useful technique for improving process efficiency in an ETL subsystem. This paper presents an innovative technique to providing a parameterized dynamic execution custom transformation that can be easily incorporated into any SAS Data Integration Studio job to provide conditional execution or process path switching capabilities | data integration, sas data integration studio, raw data | source | 15 |
| 80 | Confirmatory Factor Analysis and Structural Equation Modeling of Non-cognitive Assessments Using PROC CALIS | Steven L. Holtzman | Noncognitive assessments, which measure constructs such as time management, goal-setting and personality, are becoming more prevalent today in research within the domains of academic performance and workforce readiness. Many instruments that are used for this purpose contain a large number of items that can each be assigned to specific facets of the larger construct. The factor structure of each instrument emerges from a mixture of psychological theory and empirical research, often by performing exploratory factor analysis (EFA) using the SAS procedure PROC FACTOR. Once an initial model is established, it is important to perform confirmatory factor analysis (CFA) to confirm that the hypothesized model provides a good fit to the data. If outcome data are collected, such as grades, structural equation modeling (SEM) should also be employed to investigate how well the assessment predicts these measures |
latent variable, estimate, proc calis, proc step, sample, factor analysis, response variable, covariance, confirmatory factor analysis, residual, sample size, correlation | source | 11 |
| 81 | Considerations for Adding SAS Visual Analytics to an Existing SAS Business Intelligence Deployment | Christine Vitron, James Holman |
If you have an existing SAS Business Intelligence environment and you want to add SAS Visual Analytics, you need to make some architectural choices. SAS Visual Analytics and SAS Business Intelligence can share certain components, such as a SAS Metadata Server and the SAS Web Infrastructure Platform. Sharing metadata eliminates the need to create and maintain duplicate information, and it enables your users to take advantage of functionality that can be shared between SAS Visual Analytics and SAS Business Intelligence. Sharing the SAS Web Infrastructure Platform enables SAS middle-tier applications such as SAS Visual Analytics Services and SAS Web Report Studio to communicate with each other. Intended for SAS architects and administrators, this paper explores supported architecture for SAS Visual Analytics and SAS Business Intelligence. The paper then identifies areas where the architecture can be shared as well as where resources should be kept separate | business intelligence, line chart | source | 14 |
| 82 | Converting Clinical Database to SDTM: The SAS Implementation | Hong Chen | The CDISC Study Data Tabulation Model (SDTM) provides a standardized structure and specification for a broad range of human and animal study data in pharmaceutical research, and is widely adopted in the industry for the submission of the clinical trial data. Because SDTM requires additional variables and datasets that are not normally available in the clinical database, further programming is required to convert the clinical database into the SDTM datasets. This presentation introduces the concept and general requirements of SDTM, and the different approaches in the SDTM data conversion process. The author discusses database design considerations, implementation procedures, and SAS macros that can be used to maximize the efficiency of the process. The creation of the metadata DEFINE.XML and the final STDM dataset validation are also discussed. 2 | clinical database, sdtm domain, adam dataset, data cleaning, clinical trial, macro definition, proc contents, clinical data, cdisc | source | 5 |
| 83 | Counting Days of the Week - The INTCK Approach | Jinson J. Erinjeri | The INTCK function is used to obtain the number of time intervals between two dates. The INTCK function comes with arguments and argument-modifiers to enable us to perform variety of date related manipulations. This paper deals with a real time simple usage of INTCK function to calculate frequency of days of the week between start and end day of a trip. The INTCK function with its arguments can directly calculate the number of days of the week as illustrated in this paper. The same usage of INTCK function using PROC SQL is also presented in this paper. All the codes executed/presented in this paper involve Base 9.3 SAS only |
datepart, proc freq, proc sql, proc step, sysdate9 | source | 4 |
| 84 | Create Custom Graphs in SAS Visual Analytics Using SAS Visual Analytics Graph Builder | Ravi Devarajan, Himesh Patel, Patricia L. Berryman, Lisa Everdyke |
SAS Visual Analytics Designer enables you to create reports with different layouts. There are several basic graph objects that you can include in these reports. What if you wanted to create a report that wasn't possible with one of the out-of-the-box graph objects? No worries! The new SAS Visual Analytics Graph Builder available from the SAS Visual Analytics home page lets you create a custom graph object using built-in sample data. You can then include these graph objects in SAS Visual Analytics Designer and generate reports using them. Come see how you can create custom graph objects such as stock plots, butterfly charts, and more. These custom objects can be easily shared with others for use in SAS Visual Analytics Designer | bar chart, time series analysis, series plot, class variable, line chart, scatter plot | source | 24 |
| 85 | Creating a Journal-Ready Group Comparison Table in Microsoft Word with SAS | Alan C. Elliott | Presentation | median test, create table | source | 3 |
| 86 | Creating a SimNICU: Using Simulation to Model Staffing Needs in Clinical Environments | Chris DeRienzo, David Tanaka, Emily Lada, Phil Meanor |
Patient safety in a neonatal intensive care unit (NICU)-as in any hospital unit-is critically dependent on appropriate staffing. This project used SAS Simulation Studio to create a discrete-event simulation model of a specific NICU that can be used to predict the number of nurses needed per shift. The model incorporates the complexities inherent in determining staffing needs, including variations in patient acuity, referral patterns, and length of stay. To build the model, the group first estimated probability distributions for the number and type of patients admitted each day to the unit. Using both internal and published data, the team estimated distributions for various NICU-specific patient morbidities, including type and timing of each morbidity event and its temporal effect on a patient's acuity | source | 10 | |
| 87 | Creating Define.xml v2 Using SAS for FDA Submissions | Qinghua (Kathy) Chen, James Lenihan |
When submitting clinical data to the Food and Drug Administration (FDA), we need to submit the trial results as well as information that help the FDA understand the data. The FDA has requires the CDISC Case Report Tabulation Data Definition Specification (Define-XML), which is based on the CDISC Operational Data Model (ODM), for submissions using Study Data Tabulation Model (SDTM) for this purpose. Electronic submission to the FDA is therefore a process of following the guidelines from CDISC and FDA. This paper illustrates how to create a CDISC guidance compliant define.xml v2 from metadata using SAS | dataset, clinical data, controlled terminology, macro definition, cdisc | source | 20 |
| 88 | Creating Formats on the Fly | Suzanne M. Dorinski | The Census Bureau conducts the Common Core of Data surveys for the National Center for Education Statistics annually. We have written SAS programs to automate the database documentation. We try to avoid including hard- coded values in the programs. Thanks to a record layout spreadsheet, the analysts can quickly update the survey metadata outside the SAS programs. This paper explains how SAS can read the record layout spreadsheet to create formats on the fly. The analysts can update the values as changes occur over time without having to worry about writing correct SAS syntax. Behind the scenes, SAS is using dictionary views, macros, ODS OUTPUT, PROC TEMPLATE, PROC FORMAT, the ODS Report Writing Interface, and RTF to create the desired results. This paper uses syntax for SAS 9.2, written for programmers at the intermediate level |
proc step, proc format | source | 11 |
| 89 | Creating Journal Ready Tables with Special Characters Using ODS LaTeX | Steven H. Feder | LaTeX is a free document creation package often used to create journal articles. It provides the capability to create very specific formatting and to write a wide variety of formulas. Using ODS, SAS can write documents to a LaTeX file, which can then be compiled through LaTeX into PDF files. In this article we briefly review the basic syntax and options to produce these files. Then we look at how to create a new tagset to make changes to the standard ODS LaTeX templates to create the non- gridded table appearance typically seen in journal articles. We also explore how to write special characters and equations not otherwise available through ODS LaTeX |
sas output, dataset | source | 11 |
| 90 | Custom BI Tools Using SAS Stored Processes | Scott Hutchison, John Venturini |
Business Intelligence platforms provide a bridge between expert data analysts and decision-makers and other end-users. But what do you do when you can identify no system that meets both your needs and your budget? If you are the Consolidated Data Analysis Center in the HHS Office of Inspector General, you use SAS Enterprise BI Server and the SAS Stored Process Web Application to build your own. This paper covers the inception, design, and implementation of the PAYment by Geographic Area (PAYGAR) system, which uses only SAS Enterprise BI tools, namely the Stored Process Web Application, PROC GMAP, and HTML/JAVA embedded in a DATA STEP, to create an interactive platform for presenting and exploring data that has a geographic component |
business intelligence, dataset | source | 13 |
| 91 | Customer Perception and Reality: Unraveling the Energy Customer Equation | Mark Konya, Kathy Ball |
Energy companies that operate in a highly regulated environment and are constrained in pricing flexibility must employ a multitude of approaches to maintain high levels of customer satisfaction. Many investor-owned utilities are just starting to embrace a customer-centric business model to improve the customer experience and hold the line on costs while operating in an inflationary business setting. Faced with these challenges, it is natural for utility executives to ask: "What drives customer satisfaction, and what is the optimum balance between influencing customer perceptions and improving actual process performance in order to be viewed as a top-tier performer by our customers?" J.D. Power, for example, cites power quality and reliability as the top influencer of overall customer satisfaction. But studies have also shown that customer perceptions of reliability do not always match actual reliability experience | customer satisfaction, proc hpsplit, data mining | source | 11 |
| 92 | Customer Profiling for Marketing Strategies in a Healthcare Environment | MaryAnne DePesquo | In this new era of healthcare reform, health insurance companies have heightened their efforts to pinpoint who their customers are, what their characteristics are, what they look like today, and how this impacts business in today's and tomorrow's healthcare environment. The passing of the Healthcare Reform policies led insurance companies to focus and prioritize their projects on understanding who the members in their current population were. The goal was to provide an integrated single view of the customer that could be used for retention, increased market share, balancing population risk, improving customer relations, and providing programs to meet the members' needs. By understanding the customer, a marketing strategy could be built for each customer segment classification, as predefined by specific attributes. This paper describes how SAS was used to perform the analytics that were used to characterize their insured population | regression analysis, proc step, cluster analysis, sample, health insurance, logistic regression, proc varclus, logistic distribution, outlier, proc varreduce, correlation, proc univariate | source | 7 |
| 93 | Dashboards: A Data Lifeline for the Business | Jennifer McBride | Presentation | proc sql, sas enterprise guide | source | 7 |
| 94 | Data Cleaning: Longitudinal Study Cross-Visit Checks | Lauren Parlett | Cross-visit checks are a vital part of data cleaning for longitudinal studies. The nature of longitudinal studies encourages repeatedly collecting the same information. Sometimes, these variables are expected to remain static, go away, increase, or decrease over time. This presentation reviews the naïve and the better approaches at handling one-variable and two-variable consistency checks. For a single-variable check, the better approach features the new ALLCOMB function, introduced in SAS 9.2. For a two-variable check, the better approach uses a BY PROCESSING variable to flag inconsistencies. This paper will provide you the tools to enhance your longitudinal data cleaning process | do loop, missing value | source | 7 |
| 95 | Data Coarsening and Data Swapping Algorithms | Tom Krenzke, Katie A. Hubbell, Mamadou Diallo, Amita Gopinath, Sixia Chen |
With increased concern about privacy and, at the same time, pressure to make survey data available, statistical disclosure control (SDC) treatments are performed on survey microdata to reduce disclosure risk prior to dissemination to the public. Making the time to conduct the necessary SDC treatments is all the more problematic in the push to provide data online for immediate user query. Two SDC approaches are data coarsening, which reduces the information collected, and data swapping, which is used to adjust data values. Data coarsening includes recodes, top/bottom codes, and variable suppression. Challenges related to creating a SAS macro for data coarsening include providing flexibility for conducting different coarsening approaches and keeping track of the changes to the data so that variable and value labels can be assigned correctly. Data swapping includes selecting target records for swapping, finding swapping partners, and swapping data values for the target variables | proc format, nominal variable, ordinal variable, proc step, macro definition, missing value, response variable | source | 19 |
| 96 | Data Quality Governance for Analytics Teams | Anand Jagarapu | Presentation | data cleaning, data governance | source | 2 |
| 97 | DATA Step Merging Techniques: From Basic to Innovative | Arthur L. Carpenter | Merging or joining data sets is an integral part of the data consolidation process. Within SAS there are numerous methods and techniques that can be used to combine two or more data sets. We commonly think that within the DATA STEP the MERGE statement is the only way to join these data sets, while in fact, the MERGE is only one of numerous techniques available to us to perform this process. Each of these techniques has advantages and some have disadvantages. The informed programmer needs to have a grasp of each of these techniques if the correct technique is to be applied. This paper covers basic merging concepts and options within the DATA STEP, as well as, a number of techniques that go beyond the traditional MERGE statement. These include fuzzy merges, double SET statements, and the use of key indexing. The discussion will include the relative efficiencies of these techniques, especially when working with large data sets |
hash table, merge statement, by group processing, set statement, key, conditional do loop, proc sql, adverse events, proc step | source | 10 |
| 98 | Data Visualization in Health Care: Optimizing the Utility of Claims Data through Visual Analysis | Krisa Tailor | A revolution is taking place in the US at both the national and state level in the area of health care big data, analytics, and transparency. Large amounts of data on the health of communities, the quality of health care providers, and the cost of health care is being collected and made available by both levels of government, to a variety of stakeholders. The surfacing of these data and consumption by health care decision makers unfolds a new opportunity to view, explore, and analyze health care data in novel ways. Furthermore, these data provide the health care system an opportunity to advance the achievement of the Triple Aim. (see iti.org/tripleaim). The convergence of big data, analytics, and transparency will bring a sea change to the world of health care by necessitating new ways to communicate information to end users, such as payers, providers, researchers, and consumers | big data, insurance claims, sas visual analytics, data visualization, clinical data | source | 16 |
| 99 | Data Visualization within Management at Euramax | Peter Wijers | Are you interested in starting to use SAS VA, and seeking for actual information regarding its implementation? Looking for the do's and don'ts implementing SAS VA? Finding out new ways of presenting data to your management? Interested in the mobile reporting power of SAS VA? This paper is for all the people that are involved in the process of choosing and/or implementing SAS VA and want to get inside information regarding the experiences of Euramax Coated Products while using SAS VA for 1 year | sas visual analytics, data validation rules, excel | source | 6 |
| 100 | Debugging SAS Code in a Macro | Bruce F. Gilsen | Debugging SAS code contained in a macro can be frustrating because the SAS error messages refer only to the line in the SAS log where the macro was invoked. This can make it difficult to pinpoint the problem when the macro contains a large amount of SAS code. Using a macro that contains one small DATA STEP, this paper shows how to use the MPRINT and MFILE options along with the fileref MPRINT to write just the SAS code generated by a macro to a file. The "de-macroified" SAS code can be easily executed and debugged |
sas log, options statement | source | 5 |
| 101 | Deploying a User-Friendly SAS Grid on Microsoft Windows | Houliang Li | Your company's chronically overloaded SAS environment, adversely impacted user community, and the resultant lackluster productivity have finally convinced your upper management that it is time to upgrade to SAS grid to eliminate all the resource problems once and for all. But after the contract is signed and implementation begins, you as the SAS administrator suddenly realize that your company-wide standard mode of SAS operations, i.e., using the traditional SAS Display Manager on a server machine, runs counter to the expectation of SAS grid - your users are now supposed to switch to SAS Enterprise Guide on a PC. This is utterly unacceptable to the user community because almost everything has to change in a big way. If you like to play a hero in your little world, this is your opportunity. There are a number of things you can do to make the transition to SAS grid as smooth and painless as possible, and your users get to keep their favorite SAS Display Manager | sas grid, sas enterprise guide, batch job, work library, grid computing, sas administrator | source | 6 |
| 102 | Designing for the Mobile Workforce | Peter Ina, Khaliah Cothran |
The evolution of the mobile landscape has created a shift in the workforce that now favors mobile devices over traditional desktops. Considering that today's workforce is not always in the office or at their desks, new opportunities have been created to deliver report content through innovative mobile experiences. SAS Mobile BI for both iOS and Android tablets compliments the SAS Visual Analytics offering by providing anytime, anywhere access to reports containing information that consumers need. This paper presents best practices and tips on how to optimize reports for mobile users, taking into consideration the constraints of limited screen real estate and connectivity, as well as answers a few frequently asked questions. Discover how SAS Mobile BI captures the power of mobile reporting to prepare for the vast growth that is predicted in the future | data visualization, big data | source | 9 |
| 103 | Detecting Patterns Using Geo-Temporal Analysis Techniques in Big Data | Richard W. La Valley, Abraham Usher, Donald J. Henderson, Paul M. Dorfman |
Presentation: Detecting Patterns using Geo-temporal Analysis Techniques in Big Data Rich La Valley Abe Usher Don Henderson Paul Dorfmann Leidos HumanGeo Group LLC Henderson Consulting ISAS Consultant Arlington, Virginia Arlington, VA Olney, Maryland Jacksonville, FL . Geohash is a latitude/longitude geocode system is a hierarchical spatial data structure which subdivides space into buckets of grid shape whicho offers properties like arbitrary precision and the possibility of gradually removing characters from the end of the code to reduce its size (and gradually lose precision). GeoHashing: Timehash algorithm is a way of representing a user-defined variable precision temporal. It is an implementation of a quadtree that is defined with simple ASCII characters. . Timehasing Example Example Geohashing Analysis Geotemporal hashing example Geotemporal hashing with SAS Why is it Important? | time series analysis, data mining | source | 1 |
| 104 | Develop Highly Interactive Web Charts with SAS | Rajesh Inbasekaran, Naren Mudivarthy, Neetha Sindhu |
Very often there is a need to present the analysis output from SAS through web applications. On these occasions, it would make a lot of difference to have highly interactive charts over static image charts and graphs. Not only this is visually appealing, with features like zooming, filtering etc., enables consumers to have a better understanding of the output. There are a lot of charting libraries available in the market which enables us to develop cool charts without much effort. Some of the packages are Highcharts, Highstock, KendoUI etc. They are developed in JavaScript and use the latest HTML5 components and also support a variety of chart types such as line, spline, area, area spline, column, bar, pie, scatter, angular gauges, area range, area spline range, column range, bubble, box plot, error bars, funnel, waterfall and polar chart types etc | time series analysis, proc json | source | 6 |
| 105 | Developing the Code to Execute Particle Swarm Optimization in SAS | Anurag G. Srivastava, Sangita Kumbharvadiya |
Particle swarm optimization is a heuristic global optimization method which was given by James Kennedy and Russell C. Eberhart in 1995. (James Kennedy and Russell C. Eberhart) The purpose of this paper is to develop a code for particle swarm optimization in SAS 9.2 | artificial intelligence, do loop | source | 4 |
| 106 | Developing Web Applications with SAS Stored Processes | Philip A. Mason | macro variable, sas enterprise guide, dataset, excel, sample, proc step, proc sql | source | 26 | |
| 107 | Did My Coupon Campaign Accomplish Anything? An Application of Selection Models to Retailing | Gunce Walton, Kenneth Sanford |
Evaluation of the efficacy of an intervention is often complicated because the intervention is not randomly assigned. Usually, interventions in marketing, such as coupons or retention campaigns, are directed at customers because their spending is below some threshold or because the customers themselves make a purchase decision. The presence of nonrandom assignment of the stimulus can lead to over- or underestimating the value of the intervention. This can cause future campaigns to be directed at the wrong customers or cause the impacts of these effects to be over- or understated. This paper gives a brief overview of selection bias, demonstrates how selection in the data can be modeled, and shows how to apply some of the important consistent methods of estimating selection models, including Heckman's two-step procedure, in an empirical example. Sample code is provided in an appendix | proc calis, sample | source | 11 |
| 108 | Dining with the Data: The Case of New York City and Its Restaurants | Pruthvi Bhupathiraju Venkata, Goutam Chakraborty |
New York City boasts a wide variety of cuisine owing to the rich tourism and a large immigrant population. The quality of food and hygiene maintained at the restaurants, serving different cuisines, has a direct impact on the people dining in them. The objective of this paper is to build a model that predicts the grade of the res taurants in New York City. Our research also provides insights into the distribution of restaurants, cuisine categories, grades, criticality of violations etc. and concludes with a sequence analysis performed on the complete set of violations recorded for the restaurants at different time periods over the years 2012 and 2013. The data for 2012 is used to build the model, while the data for 2013 is used to score the model. The data set consists of 15 variables that capture restaurant background and violation details. The target is an ordinal variable with three levels, A, B, and C, in the descending order of the quality representation | regression analysis, logistic regression, data cleaning, ordinal variable, stepwise selection, logistic distribution | source | 7 |
| 109 | Direct Memory Data Management Using the APP Tools | Paul M. Dorfman | 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 physical memory in the DATA STEP. APP functions have long been a SAS dark horse. First, the examples of APP usage in SAS documentation amount to a few technical report tidbits intended for mainframe system programming, with nary a hint how the functions can be used for data management programming. Second, the documentation note on the CALL POKE routine is so intimidating in tone that many potentially receptive folks might decide to avoid the allegedly precarious route altogether |
numeric variable, character variable, sas programmer, temporary array, length statement, data governance, variable, variable list, proc sql, dataset | source | 18 |
| 110 | Disease Prevention to Reduce New Hampshire Health-Care Claims and Costs: A Data Mining Approach | Rakesh Karn, Rom Khattri, Pradeep Podila, Linda Schumacher |
Presentation | time series analysis, data mining, proc esm | source | 6 |
| 111 | Don't Get Blindsided by PROC COMPARE | Joshua M. Horstman, Roger D. Muller |
"NOTE: No unequal values were found. All values compared are exactly equal." Do your eyes automatically drop to the end of your PROC COMPARE output in search of these words? Do you then conclude that your data sets match? Be careful here! Major discrepancies may still lurk in the shadows, and you'll never know about them if you make this common mistake. This paper describes several of PROC COMPARE's blind spots and how to steer clear of them. Watch in horror as PROC COMPARE glosses over important differences while boldly proclaiming that all is well. See the gruesome truth about what PROC COMPARE does, and what it doesn't do! Learn simple techniques that allow you to peer into these blind spots and avoid getting blindsided by PROC COMPARE! |
proc compare, proc step, data cleaning | source | 7 |
| 112 | Doubling Down on Analytics: Using Analytic Results from Other Departments to Enhance Your Approach to Marketing | Natalie G. Osborn, Eric Peterson |
Response rates, churn models, customer lifetime value--today's marketing departments are more analytically driven than ever. Marketers have had their heads down developing analytic capabilities for some time. The results have been game-changing. But it's time for marketers to look up and discover which analytic results from other departments can enhance the analytics of marketing. What if you knew the demand forecast for your products? What could you do? What if you understood the price sensitivity for your products? How would this impact the actions that your marketing team takes? Using the hospitality industry as an example, we explore how marketing teams can use the analytic outputs from other departments to get better results overall | revenue management, demand forecasting, customer lifetime value, data governance, master data management, predictive analytics, customer experience, web analytics, data mining | source | 6 |
| 113 | Driving CRM Success with SAS Marketing Automation | Sven Putseys, Zelia Pellissier |
Vistaprint saw the opportunity in the printing market to get more out of high-volume printing by grouping similar orders in large groups. They heavily rely on technology to handle design, printing, and order handling and use the Internet as a medium. With their successful expansion across the world, the issue they were facing was a lot of one-time buyers and a lot of registered users who didn't finish the check-out. The need to implement a retention strategy was the next logical step, for which they chose SAS Campaign Management. In this session, Vistaprint explains how they use campaign management for retention and how the project was addressed. They will also touch on how the concept of high performance could open up new possibilities for them | by group processing, error message | source | 4 |
| 114 | Ebony and Ivory: SAS Enterprise BI and SAS Visual Analytics Living in Perfect Harmony | Ted Stolarczyk | "Ebony and Ivory" was a number one song by Paul McCartney and Stevie Wonder about making music together, proper integration, unity, and harmony on a deeper level. With SAS Visual Analytics, current SAS Enterprise BI software customers can rest assured that their years of existing work and content can coexist until they can fully transition over to SAS Visual Analytics. This presentation covers 10 inter-operability integration points between SAS Enterprise BI software and SAS Visual Analytics | sas enterprise guide, sample, excel | source | 13 |
| 115 | Effective Risk Aggregation and Reporting Using SAS | Wei Chen, Jimmy Skoglund, Srinivasan Iyer |
Both recent banking and insurance risk regulations require effective aggregation of risks. To determine the total enterprise risk for a financial institution, all risks must be aggregated and analyzed. Typically, there are two approaches: bottom-up and top-down in risk aggregation. In either approach, financial institutions face challenges due to various levels of risks with differences in metrics, data source, and availability. First, it is especially complex to aggregate risk. A common view of the dependence between all individual risks can be hard to achieve. Second, the underlying data sources can be updated at different times and can have different horizons. This in turn requires an incremental update of the overall risk view. Third, the risk needs to be analyzed across on-demand hierarchies. This paper presents SAS solutions to these challenges | sas risk engine, sas risk, enterprise risk, raw data, stress testing, sample, market risk | source | 9 |
| 116 | Effective Use of SAS Enterprise Guide in a SAS 9.4 Grid Manager Environment | Edoardo Riva | With the introduction of new features in SAS 9.4 Grid Manager, administrators of SAS solutions now have even better capabilities for effectively managing the use of SAS Enterprise Guide in a grid environment. In this paper, we explain and demonstrate proven practices for configuring the SAS 9.4 Grid Manager environment, leveraging grid options sets and grid-spawned SAS workspace servers. Further, we walk through the options provided by SAS Enterprise Guide that make the most effective use of the grid environment | sas enterprise guide, sas grid, grid computing, parallel processing, sas management console, batch job | source | 18 |
| 117 | Effectively Utilizing Loops and Arrays in the DATA Step | Arthur X. Li | The implicit loop refers to the DATA STEP repetitively reading data and creating observations, one at a time. The explicit loop, which utilizes the iterative DO, DO WHILE, or DO UNTIL statements, is used to repetitively execute certain SAS statements within each iteration of the DATA STEP execution. Utilizing explicit loops is often used to simulate data and to perform a certain computation repetitively. However, when an explicit loop is used along with array processing, the applications are extended widely, which includes transposing data, performing computations across variables, etc. Being able to write a successful program that uses loops and arrays, one needs to know the contents in the program data vector (PDV) during the DATA STEP execution, which is the fundamental concept of DATA STEP programming |
do loop, output, execution phase, array, conditional do loop, in=, out=, dataset, missing value, compilation phase, array bounds, implicit loop, temporary array, standard deviation, automatic variable, class variable, normal distribution, set statement | source | 17 |
| 118 | Efficiency Estimation Using a Hybrid of Data Envelopment Analysis and Linear Regression | John Dilip Raj | Literature suggests two main approaches , parametric and non -parametric, for constructing efficiency frontiers using which efficiency scores of other units can be based. Parametric functions can be either deterministic or stochastic in nature. However, when multiple inputs and outputs are encountered, Data Envelopment Analysis (DEA), a non -parametric approach, is a powerful tool used for decades in measurement of productivity/efficiency w ith wide range of applications. Both approaches have its advantages and limitations. This paper attempts to further explore and validate a hybrid approach, taking the best of both DEA and parametric approach, in order to estimate efficiency of Decision Making Units (DMUs) in an even better way | proc reg, regression analysis | source | 7 |
| 119 | Embedding Critical Content in an E-mail Message Body | Amy Swartz | Presentation | data cleaning, data review | source | 5 |
| 120 | Empowering Clinical Research Staff with SAS Enterprise Guide | Christopher W. Schacherer | The availability of specialized programming and analysis resources in academic medical centers is often limited, creating a significant challenge for clinical research. The current work describes how Base SAS and SAS Enterprise Guide are being used to empower research staff so that they are less reliant on these scarce resources | sas enterprise guide, sample, clinical data, macro variable, sas programming, sas programmer, dataset, excel, proc step, clinical research, sas base, oracle, proc sql, sql server | source | 20 |
| 121 | Empowering SAS Users on the SAP HANA Platform | Christoph Morgen | This session provides an overview of how SAS environments can be best integrated with SAP HANA. You'll learn what is different about SAP HANA and how SAS users can access and push-down their work, and thus start to benefit from the in-memory power of SAP HANA. We'll further explore how a SAS Predictive Modeling environment will be even closer embedded in the SAP HANA platform | proc sql, proc step, sas programmer, libname engine, macro definition, group by, sql pass-through, libname statement, macro facility, data mining, macro variable, data wrangling, %if-%then-%else | source | 9 |
| 122 | Empowering the SAS Programmer: Understanding Basic Microsoft Windows Performance Metrics by Customizing the Data Results in SAS/GRAPH Software | John Maxwell | Typically, it takes a system administrator to understand the graphic data results that are generated in the Microsoft Windows Performance Monitor. However, using SAS/GRAPH software, you can customize performance results in such a way that makes the data easier to read and understand than the data that appears in the default performance monitor graphs. This paper uses a SAS data set that contains a subset of the most common performance counters to show how SAS programmers can create an improved, easily understood view of the key performance counters by using SAS/GRAPH software. This improved view can help your organization reduce resource bottlenecks on systems that range from large servers to small workstations. The paper begins with a concise explanation of how to collect data with Windows Performance Monitor |
sas enterprise guide, line chart, sas programmer, sample, note | source | 12 |
| 123 | Enhance the ODS HTML Output with JavaScript | Yu Fu, Chao Huang |
For data analysts, one of the most important steps after manipulating and analyzing the dataset is to create a report for it. Nowadays, many statistics tables, reports are generated as HTML files and can be easily accessed through internet. However, the SAS Output Delivery System (ODS) HTML output has many limitations on interacting with users. In this paper, we introduce a method to enhance the traditional ODS HTML output by using jQuery (a JavaScript library). A macro was developed to implement this idea. Compared to the standard HTML output, this macro can add sort, pagination, search and even drilldown function to the ODS HTML output file |
ods html, dataset | source | 12 |
| 124 | Enhancing SAS Piping Through Dynamic Port Allocation | Piyush K. Singh, Gerhardt Pohl |
This paper explains how a port (in Linux SAS environment) can be chosen automatically for SAS parallel processing on remote server. Using traditional method we can assign a fixed port number for parallel processing. However, that restricts user's flexibility to choose other available port, if the assigned port is being used by some other service/process. This paper will describe how a Linux shell script can be called within SAS code to ascertain available ports that can be further used in SAS code for parallel processing | parallel processing, mp connect, proc step, macro variable | source | 6 |
| 125 | Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs | Lora D. Delwiche, Susan J. Slaughter |
This paper is based on the belief that debugging your programs is not only necessary, but also a good way to gain insight into how SAS works. Once you understand why you got an error, a warning, or a note, you'll be better able to avoid problems in the future. In other words, people who are good debuggers are good programmers. This paper covers common problems including missing semicolons and character -to-numeric conversions, and the tricky problem of a DATA STEP that runs without suspicious messages but, nonetheless, produces the wrong results. For each problem, the message is deciphered, possible causes are listed, and how to fix the problem is explained |
sas log, missing value, proc print, put statement, proc step, input statement, error message, dataset, character variable, infile statement, numeric variable, firstobs, options statement, excel, sas output | source | 15 |
| 126 | Estimating Ordinal Reliability Using SAS | Laura Kapitula | In evaluation instruments and tests individual items are often collected using an ordinal measurement or likert type scale. Typically measures such as Cronbach's alpha are estimated using the standard Pearson correlation. Gadderman, Guhn and Zumbo (2012) illustrate how using the standard Pearson correlations may yield biased estimates of reliability when the data are ordinal and present methodology for using the polychoric correlation in reliability estimates as an alternative. This session shows how to implement the methods of Gadderman, Guhn and Zumbo using SAS software. An example, will be presented that incorporates these methods in the estimation of the reliability of an active learning post-occupancy evaluation instrument developed by Steelcase Education Solutions researchers | correlation, pearson correlation, factor analysis, proc step, proc corr, proc princomp | source | 6 |
| 127 | Evaluating School Attendance Data Using SAS | Jacob Foard, Thomas Nix, Rachel Simmons |
Presentation | proc step, proc format | source | 3 |
| 128 | Ex-Ante Forecast Model Performance with Rolling Simulations | Michael J. Leonard, Ashwini Dixit, Udo Sglavo |
Given a time series data set, you can use automatic time series modeling software to select an appropriate time series model. You can use various statistics to judge how well each candidate model fits the data (in-sample). Likewise, you can use various statistics to select an appropriate model from a list of candidate models (in-sample or out-of-sample or both). Finally, you can use rolling simulations to evaluate ex-ante forecast performance over several forecast origins. This paper demonstrates how you can use SAS Forecast Server Procedures and SAS Forecast Studio software to perform the statistical analyses that are related to rolling simulations | time series analysis, sample, residual, time series data, feature selection, fullstimer, forecast horizon | source | 18 |
| 129 | Examples of Logistic Modeling with the SURVEYLOGISTIC Procedure | Rob Agnelli | Logistic regression is a powerful technique for predicting the outcome of a categorical response variable and is used in a wide range of disciplines. Until recently, however, this methodology was available only for data that were collected using a simple random sample. Thanks to the work of statisticians such as Binder (1983), logistic modeling has been extended to data that are collected from a complex survey design that includes strata, clusters, and weights. Through examples, this paper provides guidance in using PROC SURVEYLOGISTIC to apply logistic regression modeling techniques to data that are collected from a complex survey design. The examples relate to calculating odds ratios for models with interactions, scoring data sets, and producing Receiver Operating Characteristic (ROC) curves. As an extension of these techniques, a final example shows how to fit a generalized estimating equations (GEE) logit model |
odds ratio, proc step, proc surveylogistic, sample, roc curve, logistic distribution, logistic regression, auc, response variable, estimate, regression analysis, confidence interval, repeated measures, random sampling, main effect, proc genmod, proc logistic, proc surveyfreq, where statement, likelihood function, health insurance, proc contents, maximum likelihood estimation, proc freq | source | 17 |
| 130 | Excel with SAS and Microsoft Excel | Andrew Howell | Presentation | excel, sas enterprise guide, proc import, proc step, proc export, proc sql, name range list | source | 29 |
| 131 | Experiences in Using Academic Data for SAS BI Dashboard Development | Evangeline Collado, Michelle Parente |
Business Intelligence (BI) dashboards serve as an invaluable, high-level, visual reference tool for decision-making processes in many business industries. A request was made to our department to develop some BI dashboards that could be incorporated in an academic setting. These dashboards would aim to serve various undergraduate executive and administrative staff at the university. While most business data may lend itself to work very well and easily in the development of dashboards, academic data is typically modeled differently and, therefore, faces unique challenges. In this paper, the authors detail and share the design and development process of creating dashboards for decision making in an academic environment utilizing SAS BI Dashboard 4.3 and other SAS Enterprise Business Intelligence 9.2 tools. The authors also provide lessons learned as well as recommendations for future implementations of BI dashboards utilizing academic data | proof of concept, character, percent., business intelligence, sas enterprise guide | source | 10 |
| 132 | Exploring Data Access Control Strategies for Securing and Strengthening Your Data Assets Using SAS Federation Server | Mark Craver, Mike Frost |
"Potential of One, Power of All." That has a really nice ring to it, especially as it pertains to accessing all of your corporate data through one single data access point. It means the potential of having a single source for all of your data connections from throughout the enterprise. It also means that the complexities of connecting to these data assets from the various source systems throughout the enterprise are hidden from the end user. With this, however, comes the possibility of placing personally identifiable information in the hands of a user who should not have access to it. The bottom line is that there is risk and uncertainty with allowing users to have access to data that is disallowed by your existing data governance strategy | proc sql, access control, select, relational database, where statement, data governance | source | 11 |
| 133 | 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 merges and joins, and provides examples of effective coding techniques. Attendees examine the objectives and principles behind merges and joins, one-to-one merges (joins), and match-merge (equi-join), as well as the coding constructs associated with inner and outer merges (joins) and PROC SQL set operators |
hash table, proc sql, proc step, lookup, inner join, if-then-else, create table, right join | source | 14 |
| 134 | Exporting Formulas to Microsoft Excel Using the ODS ExcelXP Tagset | Joseph Skopic | SAS can easily perform calculations and export the result to Microsoft EXCEL in a report. However, sometimes you need EXCEL to have a formula or a function in a cell and not just a number. Whether it's for a boss who wants to see a SUM formula in the total cell or to have automatically updating reports that can be sent to people who don't use SAS to be completed, exporting formulas to EXCEL can be very powerful. This paper illustrates how, by using PROC REPORT and PROC PRINT along with the ExcelXP tagset, you can easily export formulas and functions into EXCEL directly from SAS. The method outlined in this paper requires Base SAS 9.1 or higher and EXCEL 2002 or later and requires a basic understanding of the ExcelXP tagset |
excel, missing value, proc step, proc report, proc print | source | 4 |
| 135 | Extracting Key Concepts from Unstructured Medical Reports Using SAS Text Analytics and SAS Visual Analytics | Greg Massey, Radhikha Myneni, Adrian Mattocks, Eric C. Brinsfield |
The growing adoption of electronic systems for keeping medical records provides an opportunity for health care practitioners and biomedical researchers to access traditionally unstructured data in a new and exciting way. Pathology reports, progress notes, and many other sections of the patient record that are typically written in a narrative format can now be analyzed by employing natural language processing contextual extraction techniques to identify specific concepts contained within the text. Linking these concepts to a standardized nomenclature (for example, SNOMED CT, ICD-9, ICD-10, and so on) frees analysts to explore and test hypotheses using these observational data. Using SAS software, we have developed a solution in order to extract data from the unstructured text found in medical pathology reports, link the extracted terms to biomedical ontologies, join the output with more structured patient data, and view the results in reports and graphical visualizations | sample, precision | source | 9 |
| 136 | Extracting the Needles from the Haystacks | Sara Boltman | Presentation | format statement, proc step, proc format, proc sql | source | 3 |
| 137 | Extreme SAS reporting II: Data Compendium and 5 Star Ratings Revisited | Louise S. Hadden | Each month, our project team delivers updated 5-Star ratings for 15,700+ nursing homes across the United States to Centers for Medicare & Medicaid Services. There is a wealth of data (and processing) behind the ratings, and this data is longitudinal in nature. A prior paper in this series, "Programming the Provider Previews: Extreme SAS Reporting" discussed one aspect of the processing involved in maintaining the Nursing Home Compare website. This paper will discuss two other aspects of our processing: creating an annual data Compendium, and extending the 5- star processing to accommodate several different output formats for different purposes. Products used include Base SAS, SAS/STAT, SG Procedures, and SAS/GRAPH. New annotate facilities in both SAS/GRAPH and the SG Procedures will be discussed. This paper and presentation will be of most interest to SAS programmers with medium to advanced SAS skills |
proc step, excel, dataset | source | 15 |
| 138 | Five Things to Do when Using SAS BI Web Services | Neetha Sindhu, Vimal Raj |
Traditionally, web applications interact with back-end databases by means of JDBC/ODBC connections to retrieve and update data. With the growing need for real-time charting and complex analysis types of data representation on these web applications, SAS computing power can be put to use by adding a SAS web service layer between the application and the database. With the experience that we have with integrating these applications to SAS BI Web Services, this is our attempt to point out five things to do when using SAS BI Web Services. 1) Input Data Sources: always enable "Allow rewinding stream" while creating the stored process. 2) Use LIBNAME statements to define XML filerefs for the Input and Output Streams (Data Sources). 3) Define input prompts and output parameters as global macro variables in the stored process if the stored process calls macros that use these parameters |
dataset, web service | source | 6 |
| 139 | Five Ways to Flip-Flop Your Data | Joshua M. Horstman | Data is often stored in highly normalized ("tall and skinny") structures that are not convenient for analysis. The SAS programmer frequently needs to transform the data to arrange relevant variables together in a single row. Sometimes this is a simple matter of using the TRANSPOSE procedure to flip the values of a single variable into separate variables. However, when there are multiple variables to be transposed to a single row, it might require multiple transpositions to obtain the desired result. This paper describes five different ways to achieve this flip-flop, explains how each method works, and compares the usefulness of each method in various situations. Emphasis is given to achieving a data-driven solution that minimizes hard-coding based on prior knowledge of the possible values each variable can have and that improves maintainability and reusability of the code |
proc transpose, proc step, response variable, by group processing, merge statement, proc sql, sas programmer, keep statement, sample | source | 9 |
| 140 | For Coders Only: The SAS Enterprise Guide Experience | Christopher J. Bost | "No way. Not gonna happen. I am a real SAS programmer." (Spoken by a Real SAS Programmer.) SAS Enterprise Guide sometimes gets a bad rap. It was originally promoted as a code generator for non-programmers. The truth is, however, that SAS Enterprise Guide has always allowed programmers to write their own code. In addition, it offers many features that are not included in PC SAS | sas enterprise guide, dataset, sas programmer, proc step | source | 9 |
| 141 | From Providing Support to Driving Decisions: Improving the Value of Institutional Research | Tuesdi Helbig, Gina Huff |
For almost two decades, Western Kentucky University's Office of Institutional Research (WKU-IR) has used SAS to help shape the future of the institution by providing faculty and administrators with information they can use to make a difference in the lives of their students. This presentation provides specific examples of how WKU-IR has shaped the policies and practices of our institution and discusses how WKU-IR moved from a support unit to a key strategic partner. In addition, the presentation covers the following topics: How the WKU Office of Institutional Research developed over time; Why WKU abandoned reactive reporting for a more accurate, convenient system using SAS Enterprise Intelligence Suite for Education; How WKU shifted from investigating what happened to predicting outcomes using SAS Enterprise Miner and SAS Text Miner; How the office keeps the system relevant and utilized by key decision makers; What the office has accomplished and key plans for the future | heatmap, data mining | source | 8 |
| 142 | From Traffic to Twitter--Exploring Networks with SAS Visual Analytics | Falko Schulz, Nascif Abousalh-Neto |
In this interconnected world, it is more important than ever to understand not just the details about your data, but also how different parts of your data are related to each other. From social networks to supply chains to text analytics, network analysis is becoming a critical requirement - and network visualization is one of the best ways to understand the results. The new SAS Visual Analytics network visualization shows links between related nodes as well as additional attributes such as color, size, or labels. This paper will explain the basic concepts of networks as well as provides detailed background information about how to use network visualizations within SAS Visual Analytics | sample, proc step, proc sql, create table, left join, network data, dataset, raw data, histogram | source | 32 |
| 143 | Generate Cloned Output with a Loop or Splitter Transformation | Liotus Laura | Based on selection criteria, the SAS Data Integration Studio loop or splitter transformations can be used to generate multiple output files. The ETL developer or SAS adm inistrator can decide which transformation is bette r suited for the design, priorities and SAS configuration at their s ite. Factors to consider are the set-up, maintenanc e and performance of the ETL job. The loop transformation requires an understanding of macros and a control table. The splitter transformation is more straight-forward an d self-documenting. If time allows, creating and ru nning a job with each transformation can provide benchmarking to mea sure performance. For comparison of these two optio ns, this paper shows an example of the same job using the lo op or splitter transformation. For added testing me trics, one can adapt the LOGPARSE SAS macro to parse the job logs | macro variable, where statement, proc sql, cpu time, parallel processing, macro definition, proc step | source | 12 |
| 144 | Generating Dynamic Tables Using PROC SQL and PROC TABULATE | Lovedeep Gondara | PROC TABULATE is the most widely used reporting tool in SAS, along with PROC REPORT. Any kind of report with the desired statistics can be produced by PROC TABULATE or PROC REPORT. In clinical studies summary table commonly known as Table-1 is produced for summary statistics for various variables of interest. Comparison between different treatment groups can also be reported using P-values from different statistical tests. This is usually done by running different Procedures depending on type of test required and type of variable used and then grouping the output manually in a word processor software which can be a tedious task especially if your dataset changes over time or if it is a periodical report. In here we introduce a macro that is capable of performing all major statistical tests and then combining their output in a single dataset that can be output as a formatted summary report using PROC TABULATE or PROC REPORT |
proc step, proc report, proc tabulate, proc means, nominal variable, %if-%then-%else | source | 8 |
| 145 | Geo Reporting: Integrating ArcGIS Maps in SAS Reports | Nathan Clausen, Aaron House |
This paper shares our experience integrating two leading data analytics and GIS software products - SAS and ArcGIS - to provide integrated reporting capabilities. SAS is a powerful tool for data manipulation and statistical analysis. ArcGIS is a powerful tool for analyzing data spatially and presenting complex cartographic representations. Combining statistical data analytics and Geographic Information Systems (GIS) provides increased insight into data and allow for new and creative ways of visualizing the results. While products exist to facilitate the sharing of data between SAS and ArcGIS, there are no ready-made solutions for integrating the output of these two tools in a dynamic and automated way. Our approach leveraged the individual strengths of SAS and ArcGIS, as well as the report delivery infrastructure of SAS Information Delivery Portal. 1 ArcGIS Server 2 Our configuration is based on a local installation of ArcGIS Desktop on the SAS Application Server | proc step, macro variable | source | 4 |
| 146 | Getting Started with the SAS/IML Language | Rick Wicklin | Do you need a statistic that is not computed by any SAS procedure? Reach for the SAS/IML language! Many statistics are naturally expressed in terms of matrices and vectors. For these, you need a matrix language. This paper introduces the SAS/IML language to SAS programmers who are familiar with elementary linear algebra. The focus is on statements that create and manipulate matrices, read and write data sets, and control the program flow. The paper demonstrates how to write user-defined functions, interact with other SAS procedures, and recognize efficient programming techniques |
proc iml, proc step, sample, likelihood function, maximum likelihood estimation, estimate, dataset | source | 21 |
| 147 | Getting the Most Out of SAS Visual Analytics: Design Tips for Creating More Stunning Reports | Keith Renison | Have you ever seen SAS Visual Analytics reports that are somehow more elegant than a standard report? Which qualities make reports easier to navigate, more appealing to the eye or reveal insights more quickly? These quick tips will reveal several SAS Visual Analytics report design characteristics to help make your reports stand out from the pack. We cover concepts like color palettes, content organization, interactions, labeling, and branding, to name just a few | bar chart, line chart | source | 8 |
| 148 | Getting the Warm and Fuzzy Feeling with Inexact Matching | Toby Dunn | With the ever increasing proliferation of disparate complex data being collected and stored, it has never been more important that this information is accurate, clean, integrated, and often times in compliance with an expanding set of government regulations. This means that the data must be cleaned and standardized, duplicates must be identified and removed, and the individual data must be able to be joined or merged together in some way. However, it is often the case that this data does not have the same variables or values to make this possible with a simple Join or Merge. To that end, one has to employ a set of fuzzy logics or fuzzy matching. Simply put, fuzzy matching is the implementation of algorithmic processes (fuzzy logic) to determine the similarity between elements of data such as business names, people names, or address information | create table, proc sql, proc step, data merging | source | 20 |
| 149 | Got Randomness? SAS for Mixed and Generalized Linear Mixed Models | David A. Dickey | SAS PROC GLIMMIX fits generalized linear mixed models for nonnormal data with random effects, thus combining features of both PROC GENMOD and PROC MIXED. I will review the ideas behind PROC GLIMMIX and offer examples of Poisson and binary data. PROC NLMIXED also has the capacity to fit these kinds of models. After a brief introduction to that procedure, I will show an exampl e of a zero-inflated Poisson model, which is a model that is Poisson for counts 1,2,3,…, but has more 0s than is consistent with the Poisson. This paper was previously presented at the 2010 SAS Global Forum |
poisson distribution, proc step, proc mixed, binomial distribution, likelihood function, proc glimmix, fixed effects, sample, proc calis, estimate, restricted maximum likelihood, logistic distribution, normal distribution, covariance, beta distribution, calendar year, numeric functions, maximum likelihood estimation, regression analysis, proc genmod, main effect | source | 12 |
| 150 | Graphing Made Easy with ODS Graphics Procedures | Lora D. Delwiche, Susan J. Slaughter |
New with SAS 9.2, ODS Graphics introduces a whole new way of generating graphs using SAS. With just a few lines of code, you can create a wide variety of high-quality graphs. This paper covers the three basic Statistical Graphics (SG) procedures-SGPLOT, SGPANEL and 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 |
scatter plot, bar chart, histogram, proc sgpanel, density plot | source | 19 |
| 151 | Graphs Useful for Variable Selection in Predictive Modeling | Bob Moore | This paper illustrates some SAS graphs that can be useful for variable selection in predictive modeli ng. Analysts are often confronted with hundreds of candidate var iables available for use in predictive models, and this paper will illustrate some simple SAS graphs that are easy to create and are useful for visually evaluating candi date variables for inclusion or exclusion in predictive models. T he graphs illustrated in this paper are bar charts with confidence intervals using the GCHART procedure and comparativ e histograms using the UNIVARIATE procedure. The g raphs can be used for most combinations of categorical or continuous target variables with categorical or co ntinuous input variables. This paper assumes the reader is famili ar with the basic process of creating predictive mo dels using multiple (linear or logistic) regression |
confidence interval, response variable, bar chart, histogram, macro variable, proc step, proc gchart, horizontal bar chart, feature selection, nominal variable, interval variable, data mining, proc univariate, proc freq | source | 9 |
| 152 | Handling Missing Data Using SAS Enterprise Guide | Elena Shtern, Matt Hall |
Missing data is an ever-present issue, and analysts should exercise proper care when dealing with it. Depending on the data and analytical approach, this problem can be addressed by simply removing records with missing data. However, in most cases, this is not the best approach. In fact, this can potentially result in inaccurate or biased analyses. The SAS programming language offers many DATA STEP processes and functions for handling missing values. However, some analysts might not like or be comfortable with programming. Fortunately, SAS Enterprise Guide can provide those analysts with a number of simple built-in tasks to discover missing data and diagnose their distribution across fields. In addition, various techniques are available in SAS Enterprise Guide to impute missing values, varying from simple built-in tasks to more advanced tasks that may require some customized SAS code |
missing value, sas enterprise guide, time series analysis, sas enterprise guide project, proc means, time series data, sas library, proc step | source | 21 |
| 153 | Hash It Out with a Web Service and Get the Data You Need | John Vickery | Have you ever needed additional data that was only accessible via a web service in XML or JSON? In some situations, the web service is set up to only accept parameter values that return data for a single observation. To get the data for multiple values, we need to iteratively pass the parameter values to the web service in order to build the necessary dataset. This paper shows how to combine the SAS hash object with the FILEVAR= option to iteratively pass a parameter value to a web service and input the resulting JSON or XML formatted data | web service, hash table, hash iterator, macro variable, input statement, sysrc, infile statement, proc step, proc http, macro quoting function, substr function, libname engine, by group processing | source | 11 |
| 154 | Have it Your Way: Creating Reports with the DATA Step Report Writing Interface | Peter J. Lund | SAS provides some powerful, flexible tools for creating reports, like PROC REPORT and PROC TABULATE. With the advent of the Output Delivery System (ODS) you have almost total control over how the output from those procedures looks. But, there are still times where you need (or want) just a little more and that's where the Report Writing Interface (RWI) can help. The Report Writing Interface is just a fancy way of saying you're using the ODSOUT object in a DATA STEP. This object allows you to layout the page, create tables, embed images, add titles and footnotes and more - all from within a DATA STEP, using whatever DATA STEP logic you need. Also, all the style capabilities of ODS are available to you so that your DATA STEP created output can have fonts, sizes, colors, backgrounds and borders to make your report look just like you want |
data step, ods pdf, proc report, proc step, calendar year, output delivery system, proc template, set statement, create table | source | 27 |
| 155 | Healthcare Services Data Distribution, Transformation, and Model Fitting | Dawit Mulugeta, Jason Greenfield, Tison Bolen, Lisa Conley |
Healthcare services data on products and services come in different shapes and forms. Data c leaning, characterization, massaging and transformation are essential precursors to any statistical model building efforts. In addition, data size, quality and distribution influence model se lection, model life cycle and the ease with which business insights are extracted from data. Analysts need to examine data characteristics and determine the right data transformation and methods of analysis for valid interpretation of results. In this presentation we will demonstrate the common data distribution types for a typical healthcare services industry such as Cardinal Health and their salient features | data transformation, regression analysis, proc reg, simple linear regression, logistic regression, logistic distribution, predictor variable, raw data, normal distribution, odds ratio, standard deviation, r-squared, response variable, cv, binomial distribution, residual, residual analysis, outlier, correlation, normality test, poisson distribution, data science | source | 22 |
| 156 | Help Me! Switch to SAS Enterprise Guide from Traditional SAS | Andy Ravenna | When first presented with SAS Enterprise Guide, many existing SAS programmers do not know where to begin and want to understand "What's in it for me?" if they switch over. These longtime users of SAS are accustomed to entering all their code into the Program Editor window and clicking Submit. This paper introduces SAS Enterprise Guide 6.1 to old and new users of SAS who need to code. It even points out advantages and tips that will demonstrate why a user would be excited about the switch. This paper focuses on the key points of a session involving coding and introduces new features. It also covers the top items for a user to consider when switching over to a server-based environment. Attendees will return to the office with a new motivation and confidence to start coding with SAS Enterprise Guide | sas enterprise guide, excel, sas administrator, libname statement, proc step | source | 11 |
| 157 | Helpful Hints for Transitioning to SAS 9.4 | Cindy Taylor | A group tasked with testing SAS software from the customer perspective has gathered a number of helpful hints for SAS 9.4 that will smooth the transition to its new features and products. These hints will help with the "huh?" moments that crop up when you're getting oriented and will provide short, straightforward answers. And we also share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS Customer Experience Testing shares insiders' practical tips to ensure you are ready to begin your transition to SAS 9.4 | sas studio, customer experience | source | 10 |
| 158 | Helpful Tips for Novice Mainframe Programmers | Rahul Pillay | Presentation: Do you create reports via mainframe? Use SAS as the one stop shop for all your data manipulations. SAS can efficiently read in data, create datasets without the need for multiple data steps and produce EXCEL reports without the need for edits. This poster is intended to help provide novice mainframe programmers with helpful tips to efficiently create reports using SAS in the mainframe environment. Topics covered will be Replacing JCL with SAS for Reading in Data, Efficient Merging for Efficient Programming, using PROC FREQ for Data Quality and PROC TABULATE for Superior Reporting. Replacing JCL with SAS Merging with Efficiency Maintaining Data Quality Superior Reporting Syntax |
source | 2 | |
| 159 | High-Performance Forecasting Using SAS Grid Manager | Michael J. Leonard, Cheryl Doninger, Udo Sglavo |
Many organizations need to forecast large numbers of time series that are organized in a hierarchical fashion. Good forecasting practices recommend that several hierarchies be used and that each hierarchy contain a homogeneous set of time series with similar statistical properties. Modeling and forecasting homogeneous time series hierarchies provide better out-of-sample forecast performance. Because an organization might have many time series hierarchies, it is often desirable to model and forecast these hierarchical time series in parallel for computational efficiency. Also, it is often desirable to aggregate forecasts from several nonhomogeneous time series hierarchies for report generation. This paper demonstrates these techniques for forecasting time series hierarchies in parallel and aggregating the forecasts by using SAS Forecast Server and SAS Grid Manager | time series analysis, macro parameter, sas grid, sample, time series data | source | 6 |
| 160 | How Predictive Analytics Turns Mad Bulls into Predictable Animals | Marcel Baumgartner | Portfolio segmentation is a prerequisite in all forecasting projects. Not all products are equally predictable. Nestlé uses animal names for its segmentation, and the animal behavior translates well into how the planners should plan these products. Mad Bulls are those products that are tough to predict, if we do not know what is causing their volatility. The Horses are easier to deal with. Modern time series based statistical forecasting methods can tame Mad Bulls, as they allow adding explanatory variables into the models. Nestlé now complements its SAP based Demand Planning solution with predictive analytics technology provided by SAS, to overcome these issues in an industry that is highly promotion-driven | demand forecasting, predictor variable, time series analysis | source | 5 |
| 161 | How SAS and Teradata Dramatically Improve Data Analysis at CMS | Richard Andrews | SAS offers advanced analytics while Teradata has developed one of the fastest databases known to mankind. The Office of the Actuary at CMS uses SAS and Teradata to perform many important tasks such as setting Medicare Advantage rates for providers, forecasting the cost of closing the Part-D Donut Hole, and estimating the future cost of healthcare in America. The major advantage to the Teradata platform is the speed at which data can be summarized as compared to legacy systems (billions of rows of data can be summarized in seconds where it once took days) and the SAS system provides many options for accessing and analyzing this data - whether you're on a mainframe, Windows , or Unix through SAS Enterprise Guide or SAS Enterprise Business Intelligence | proc sql, sas enterprise guide, dataset, business intelligence, length statement, proc step, sas function, sas log, data mining, in-database processing, teradata, sas programming | source | 11 |
| 162 | How SAS BI May Help You to Optimize Business Processes | Plinio Faria | This case study shows how SAS Enterprise Guide and SAS Enterprise BI made it possible to easily implement reports of fraud prevention in BF Financial Services and also how to help operational areas to increase efficiency through automation of information delivery. The fraud alert report was made using a program developed in SAS Enterprise Guide to detect frauds on loan applications and later published in SAS Web Report Studio in order to be analyzed by a team. The second example is the automation by SAS BI of a payment report that spent 30% of the time of a six-worker staff | sas enterprise guide, binary variable | source | 3 |
| 163 | How to be a Data Scientist Using SAS | Chuck Kincaid | The role of the Data Scientist is the viral job description of the decade. And like LOLcats, there are many types of Data Scientists. What is this new role? Who is hiring them? What do they do? What skills are required to do their job? What does this mean for the SAS programmer and the statistician? Are they obsolete? And finally, if I am a SAS user, how can I become a Data Scientist? Come learn about this "job of the future" and what you can do to be part of it | data science, machine learning, data mining, big data, proc sql, association rules | source | 13 |
| 164 | How to Create a SAS Enterprise Guide Custom Task to Get Data from a SharePoint List into a SAS Data Set | Bill Reid | Do you have data in SharePoint that you would like to run analysis on with SAS? This workshop teaches you how to create a custom task in SAS Enterprise Guide in order to find, retrieve, and format that data into a SAS data set for use in your SAS programs | sas enterprise guide, sample | source | 17 |
| 165 | How to Interpret SVD Units in Predictive Models? | Murali Krishna Pagolu, Goutam Chakraborty |
Recent studies suggest that unstructured data such as customer comments or feedback can enhance the power of existing predictive models. SAS Text Miner can generate SVD (Singular value decomposition) units from text documents which is a vectorial representation of terms in documents. These SVDs when used as additional inputs along with the existing structured input variables often prove to capture the response better. However, SVD units are sort of black box variables and are not easy to interpret or explain. This is a big hindrance to win over the decision makers in the organizations to incorporate these derived textual data components in the models. In this paper, we demonstrate a new and powerful feature in SAS Text Miner 12.1 which helps in explaining the SVDs or the text cluster components. We discuss two important methods useful to interpret them | response variable, proc hpsplit, neural network, network optimization, regression analysis, odds ratio, proc freq | source | 10 |
| 166 | How to Read, Write, and Manipulate SAS Dates | Jenine Milum | No matter how long you've been programming in SAS, using and manipulating dates still seems to require effort. Learn all about SAS dates, the different ways they can be presented, and how to make them useful. This paper includes excellent examples for dealing with raw input dates, functions to manage dates, and outputting SAS dates into other formats. Included is all the date information you will need: date and time functions, Informats, and arithmetic operations | sysdate9, datepart, date variable | source | 8 |
| 167 | How to Separate Regular Prices from Promotional Prices? | Michael J. Leonard, Michele A. Trovero |
Retail price setting is influenced by two distinct factors: the regular price and the promotion price. Together, these factors determine the list price for a specific item at a specific time. These data are often reported only as a singular list price. Separating this one price into two distinct prices is critical for accurate price elasticity modeling in retail. These elasticities are then used to make sales forecasts, manage inventory, and evaluate promotions. This paper describes a new time-series feature extraction utility within SAS Forecast Server that allows for automated separation of promotional and regular prices | time series analysis, missing value, price elasticity, series plot, time series data, cluster analysis | source | 7 |
| 168 | How will Big Data change Healthcare Delivery and the Life Sciences Industry? | Mark Blatt | big data, statistical software, data governance | source | 6 | |
| 169 | I Didn't Know SAS Enterprise Guide Could Do That! | Mark Allemang | This presentation is for users who are familiar with SAS Enterprise Guide but might not be aware of the many useful new features added in versions 4.2 and beyond. For example, SAS Enterprise Guide allows you to: Format your SAS source code to make it easier to read. Easily schedule a project to run at a given time. * Work with OLAP data in your enterprise. We will overview these and other features to help you become even more productive using this powerful application | sas enterprise guide, parallel processing, sas base, grid computing, proc step, proc sql | source | 18 |
| 170 | I Object: SAS Does Objects with DS2 | Peter W. Eberhardt, Xue Yao |
The DATA STEP has served SAS programmers well over the years, and although it is power ful, it has not fundamentally changed. With DS2, SAS has introduced a significant alternative to the DATA STEP by introducing an object -oriented programming environment. In this paper, we share our experiences with getting started with DS2 and learning to use it to access, manage, and share data in a scalable, threaded, and standards -based way |
missing value, macro function, macro variable, sas function, proc step, dataset, sas programmer, proc sql | source | 27 |
| 171 | I Want the Latest and Greatest! The Top Five Things You Need to Know about Migration | Josh Hames, Gerry Nelson |
Determining what, when, and how to migrate SAS software from one major version to the next is a common challenge. SAS provides documentation and tools to help make the assessment, planning, and eventual deployment go smoothly. We describe some of the keys to making your migration a success, including the effective use of the SAS Migration Utility, both in the analysis mode and the execution mode. This utility is responsible for analyzing each machine in an existing environment, surfacing product-specific migration information, and creating packages to migrate existing product configurations to later versions. We show how it can be used to simplify each step of the migration process, including recent enhancements to flag product version compatibility and incompatibility | data cleaning, credit migration, sas enterprise guide | source | 17 |
| 172 | Implementing Multiple Comparisons on Pearson Chi-square Test for an RxC Contingency Table in SAS | Man Jin, Binhuan Wang |
This paper illustrates a permutation method for implementing multiple comparison s on Pearson's Chi-square test for an R× C contingency table, using the SAS PROCEDURE FREQ and a newly developed SAS macro called CHISQ_MC. This method is analogous to the Tukey-type multiple comparison method for one-way analysis of variance |
chi-square test, proc freq, null hypothesis, proc step, frequency analysis, response variable, macro definition, anova, post hoc test | source | 6 |
| 173 | Improving the Thermal Efficiency of Coal-Fired Power Plants: A Data Mining Approach | Thanrawee Phurithititanapong, Jongsawas Chongwatpol |
Power producers are looking for ways not only to improve efficiency of power plant assets but also to grow concerns about the environmental impacts of power generation without compromising their market competitiveness. To meet this challenge, this study demonstrates the application of data mining techniques for process optimization in a coal-fired power plant in Thailand with 97,920 data records. The main purpose is to determine which factors have a great impact on both (1) heat rate (kJ/kWh) of electrical energy output and (2) opacity of the flue gas exhaust emissions. As opposed to the traditional EXCEL-based regression analysis currently employed at the plant, more complex analytical models using SAS Enterprise Miner TM help supporting managerial decision to improve the overall performance of the existing energy infrastructure while reduce emissions through a change in the energy supply structure |
proc hpsplit, data mining, regression analysis, neural network, predictor variable, stepwise selection, correlation, sample | source | 12 |
| 174 | Increase Pattern Detection in SAS Graph Template Language with New Categorical Histograms and Color-Coded Asymmetric Violin Plots | Perry Watts | Presentation | histogram, proc step, density plot | source | 11 |
| 175 | Institutional Research: Serving University Deans and Department Heads | Matthew Foraker | Administrators at Western Kentucky University rely on the Office of Institutional Research to perform detailed statistical analyses to deepen the understanding of issues associated with enrollment management, student and faculty performance, and overall program operations. This paper presents several instances of analyses performed for the university to help it identify and recruit suitable candidates into its field, uncover root causes in grade and enrollment trends, evaluate faculty effectiveness, and assess the impact of student characteristics, programs or student activities on retention and graduation rates. The paper briefly discusses the history of Institutional Research. It provides an overview of the data infrastructure created and utilized by the Office of Institutional Research at Western Kentucky University. It then presents five projects undertaken in support of university operations. One study examines the impact of students changing their majors on graduation rates | proc reg, regression analysis, data warehouse | source | 11 |
| 176 | Integrated Big Data: Hadoop + DBMS + Discovery for SAS High-Performance Analytics | John Cunningham | SAS High Performance Analytics (HPA) is a significant step forward in the area of high-speed, analytic processing, in a scalable clustered environment. However, Big Data problems generally come with data from lots of data sources, at varying levels of maturity. Teradata's innovative Unified Data Architecture (UDA) represents a significant improvement in the way that large companies can think about Enterprise ever, Data Management, including the Teradata Database, Hortonworks Hadoop, and Aster Data Discovery platform in a seamless integrated platform. Together, the two platforms provide business users, analysts, and data scientists with the ideally suited data management platforms, targeted specifically to their analytic needs, based upon analytic use cases, managed in a single integrated enterprise data management environment | high-performance sas, data governance, data mart, sas visual analytics, data science, parallel processing, teradata, data warehouse, proc sql | source | 9 |
| 177 | Integrating and Using Hierarchical Vocabularies in SAS | Glenn Colby | Controlled vocabularies define a common set of concepts that retain their meaning across contexts, supporting consistent use of terms to annotate, integrate, retrieve, and interpret information. Controlled vocabularies are often large hierarchical structures that cannot be represented using typical SAS practices (e.g., SAS format statements and hash objects). This paper compares and contrasts three models for representing hierarchical structures using SAS datasets: adjacency list, path enumeration, and nested set (Celko, 2004; Mackey, 2002). Specific controlled vocabularies discussed include a large university organizational structure and several biological vocabularies (MeSH, NCBI Taxonomy, and GO). This paper presents sample data models and SAS code for populating tables and performing queries | data standard, foreign key, sample, key, create table, dataset, where statement, relational database, proc sql, macro definition, macro facility, macro variable, proc step, %if-%then-%else | source | 22 |
| 178 | Integrating Your Corporate Scheduler with Platform Suite for SAS or SAS Grid Manager | Paul Northrop | SAS solutions are tightly integrated with the scheduling capabilities provided by SAS Grid Manager and Platform Suite for SAS. Many organizations require that their corporate scheduler be used to control SAS processing within the enterprise. Historically this has been a laborious process, requiring duplication of job and flow information using manual forms and cumbersome change management. This paper provides proven techniques and methods that enable tight integration between the corporate scheduler and SAS without the administrative overhead. Platform Suite for SAS can be used to create flows, which are then executed by the corporate scheduler. The business unit can tweak the flow without reference to the enterprise scheduling team. The approach discussed is to use the corporate scheduler to: Trigger SAS flows and to respond to flow return codes. Restart a SAS flow that has exited due to error conditions | sysrc, sas flow, sas grid | source | 23 |
| 179 | Intermittent Demand Forecasting and Multi-tiered Causal Analysis | Edward Katz | The use, limits, and misuse of statistical models in different industries are propelling new techniques and best practices in forecasting. Until recently, many factors such as data collection and storage constraints, poor data synchronization capabilities, technology limitations, and limited internal analytical expertise have made it impossible to forecast intermittent demand. In addition, integrating consumer demand data (that is, point-of-sale [POS]/syndicated scanner data from ACNielsen/ Information Resources Inc. [IRI]/Intercontinental Marketing Services [IMS]) to shipment forecasts was a challenge. This presentation gives practical how-to advice on intermittent forecasting and outlines a framework, using multi-tiered causal analysis (MTCA), that links demand to supply. The framework uses a process of nesting causal models together by using data and analytics | causal inference, predictor variable, marketing mix, sample, regression analysis, time series analysis, multiple regression, proc reg | source | 11 |
| 180 | Internal Credit Ratings.Industry's Norms and How To Get There with SAS | Boaz Galinson | Lending is the core business of most banks. One of the key success factors in credit is to be able to differentiate between the good borrowers and the bad ones by using credit ratings you can trust. I'll focus more on the corporate and high-end commercial portfolio which typically is considerably more complex than the credit retail portfolio. These portfolios are less homogenous, with a rich variety of business needs, credits and collateral, and are usually exposed to significant concentration risks (single and group borrower, sectoral , geographic etc.) and therefore require development of a specific methodology and IT tools. Industry Norms The best known credit rating is risk from the borrower. It provides a way to differentiate between "good" and bad credit. But, the whole picture is much more than the risk level, since when a borrower defaults, he might owe us much more than what he is utilizing now and the bank will usually exercisecollateral held in order to reduce the loss | credit risk, concentration risk | source | 5 |
| 181 | Internet Gambling Behavioral Markers: Using the Power of SAS Enterprise Miner 12.1 to Predict High-Risk Internet Gamblers | Sai Vijay Kishore Movva, Vandana Reddy, Goutam Chakraborty |
Using 4,056 subscribers' data about actual gambling behavior over the Internet, we develop ed behavioral markers which can be used to predict the level of risk that a subscriber is prone to gambling addiction. SAS Enterprise Miner 12.1 is used to bu ild a set of models to predict which subscriber is likely to become a high -risk internet gambler. The data contains 114 variables such as "first active date" and "first active product used" on the website as well as the characteristics of the game such as fixed odds, poker, casino, games, etc. Other measures of a subscriber's data such as money put at stake and what odds are being bet are also included in the analysis . These variables provide a comprehensive view of a subscriber's behavior while gambling ov er the website. The target variable is modeled as a binary variable, 0 indicating a risky gambler and 1 indicating a controlled gambler | regression analysis, stepwise selection, data mining, proc hpsplit, missing value, neural network, roc curve, response variable, data cleaning | source | 11 |
| 182 | Introduction to Frailty Models | John Amrhein | This paper introduces frailty models and their use in biostatistics to model time-to-event or survival data. Frailty models are survival models with at least one random effect. There are two main types of frailty models, univariate and multivariate. In a univariate model, the random effect pertains to an individual with one observation and accounts for unobserved heterogeneity at the level of the individual. The NLMIXED Procedure fits univariate models. In a multivariate model, the random effect models covariance associated with clustered observations. The clusters might be multiple individuals in a naturally occurring group or multiple measurements within an individual. These models are sometimes referred to as shared frailty models. The PHREG and NLMIXED Procedures fit multivariate frailty models |
frailty model, proc mixed | source | 9 |
| 183 | Introduction to SAS Decision Management | Steve Sparano, Charlotte Crain, David R. Duling |
Organizations today make numerous decisions within their businesses that affect almost every aspect of their daily operations. Many of these decisions are now automatically generated by sophisticated enterprise decision management systems. These decisions include what offers to make to customers, sales transaction processing, payment processing, call center interactions, industrial maintenance, transportation scheduling, and thousands of other applications that all have a significant impact on the business bottom line. Concurrently, many of these same companies have developed or are now developing new or additional analytical models that provide valuable insight into their customers, their products, and their markets. Unfortunately, many of the decision systems cannot maximize the power of analytics in the business processes at the point where the decisions are made | data validation rules, web service, data mining, dataset, training set, logistic distribution | source | 18 |
| 184 | Is Nonlinear Regression Throwing You a Curve? New Diagnostic and Inference Tools in the NLIN Procedure | Biruk Gebremariam | The NLIN procedure fits a wide variety of nonlinear models. However, some models can be so nonlinear that standard statistical methods of inference are not trustworthy. That's when you need the diagnostic and inferential features that were added to PROC NLIN in SAS/STAT 9.3, 12.1, and 13.1. This paper presents these features and explains how to use them. Examples demonstrate how to use parameter profiling and confidence curves to identify the nonlinear characteristics of the model parameters. They also demonstrate how to use the bootstrap method to study the sampling distribution of parameter estimates and to make more accurate statistical inferences. This paper highlights how measures of nonlinearity help you diagnose models and decide on potential reparameterization. It also highlights how multithreading is used to tame the large number of nonlinear optimizations that are required for these features |
bootstrap, proc nlin, regression analysis, confidence interval, estimate, histogram, proc step | source | 16 |
| 185 | Item Response Theory: What It Is and How You Can Use the IRT Procedure to Apply It | Xinming An, Yiu-Fai Yung |
Item response theory (IRT) is concerned with accurate test scoring and development of test items. Y ou design test items to measure various kinds of abilities (such as math ability), traits (such as extroversion), or behavioral characteristics (such as purchasing tendency). Responses to test items can be binary (such as correct or incorrect responses in ability tests) or ordinal (such as degree of agreement on Likert scales). Traditionally, IRT models have been used to analyze these types of data in psychological assessments and educational testing. With the use of IRT models, you can not only improve scoring accuracy but also economize test administration by adaptively using only the discriminative items. These features might explain why in recent years IRT models have become increasingly popular in many other fields, such as medical research, health sciences, quality-of-life research, and even marketing research | estimate, correlation, logistic distribution | source | 14 |
| 186 | Iterative Programming In-Database Using SAS Enterprise Guide Query Builder | Frank Capobianco | Traditional SAS programs typically consist of a series of SAS DATA steps, which refine input data sets until the final data set or report is reached. SAS DATA steps do not run in-database. However, SAS Enterprise Guide users can replicate this kind of iterative programming-and have the resulting process flow run in-database-by linking a series of SAS Enterprise Guide Query Builder tasks that output SAS views pointing at data that resides in a Teradata database, right up to the last Query Builder task, which generates the final data set or report. This session both explains and demonstrates this functionality | sas enterprise guide, view | source | 5 |
| 187 | Jazz It Up a Little with Formats | Brian J. Bee | Formats are an often under-valued tool in the SAS toolbox. They can be used in just about all domains to improve the readability of a report, or be used as a look-up table to re-code your data. SAS, out of the box, includes a multitude of ready-defined formats that can be applied without modification to address most recode/re-display requirements; and if that's not enough, there is also a FORMAT procedure for defining your own custom formats. This paper will look at using some of the SAS-supplied formats in some innovative ways, but will primarily focus on the techniques we can apply in creating our own custom formats |
proc format, proc step, proc report | source | 9 |
| 188 | Let SAS Handle Your Job While You Are Not at Work! | Anjan Matlapudi | Report automation and scheduling are very hot topics in many industries. Automating reports have many advantages including reduced work load, elimination of repetitive tasks, generate accurate results, and better performance. This paper illustrates how to design an appropriate program to automate and schedule reports in Base SAS and SAS Enterprise Guide using SAS server as well as the Windows Scheduler. The automation part includes good aspects of formatting Microsoft EXCEL using XML or VBA coding and conditional auto e-mailing with file attachments. This paper illustrates important aspect of scheduling reports as well using Microsoft Office Add-Ins to automat reports without using much manual intervention |
excel, sas enterprise guide, proc report, dataset, sas base, proc step | source | 12 |
| 189 | Let SAS Power Your .NET GUI | Matthew Duchnowski | Despite its popularity in recent years, .NET development has yet to enjoy the quality and depth of statistical support that has always been provided by SAS. And yet, many .NET applications could benefit greatly from the power of SAS and, likewise, some SAS applications could benefit from friendly graphical user interfaces (GUIs) supported by Microsoft's .NET Framework. The author aims to outline the basic mechanics of automating SAS with .NET and provide some specific strategies for maintaining parallelism between the two platforms at runtime. This will include embedding SAS scripts within a .NET project and managing communications between the two platforms. Specifically, the log and listing output will be captured and handled by .NET, and user actions will be interpreted and sent to the SAS engine. Visual Basic .NET is the author's language of choice, but an identical approach can be executed using C# syntax | macro variable, unique key | source | 10 |
| 190 | Let the CAT Catch a STYLE | Yanhong Liu, Justin Bates |
Being flexible and highlighting important details in your output is critical. The use of ODS ESCAPECHAR allows the SAS programmer to insert inline formatting functions into variable values through the DATA STEP, and it makes for a quick and easy way to highlight specific data values or modify the style of the table cells in your output. What is an easier and more efficient way to concatenate those inline formatting functions to the variable values? This paper shows how the CAT functions can simplify this task |
character functions, numeric variable, proc step | source | 11 |
| 191 | Leveraging Advanced Analytics to Create Customer-Centric Assortments | Christopher Matz | Traditional merchandise planning processes have been primarily product and location focused, with decisions about assortment selection, breadth and depth, and distribution based on the historical performance of merchandise in stores. However, retailers are recognizing that in order to compete and succeed in an increasingly complex marketplace, assortments must become customer -centric. Advanced analytics can be leveraged to generate actionable insights into the relevance of merchandise to a retailer's variou s customer segments and purchase channel preferences. These insights enrich the merchandise and assortment planning process. This paper describes techniques for using advanced analytics to impact customer -centric assortments. Topics covered include approaches for scoring merchandise based on customer relevance and preferences, techniques for gaining insight into customer relevance without customer data, and an overall approach to a customer -driven merchandise planning process | transaction data, proc means, proc freq, sample, customer segmentation, sas enterprise guide, likelihood function | source | 10 |
| 192 | Leveraging Ensemble Models in SAS Enterprise Miner | Miguel M. Maldonado, Jared Dean, Wendy Czika, Susan Haller |
Ensemble models combine two or more models to enable a more robust prediction, classification, or variable selection. This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as rotational forest and fuzzy clustering. The examples section presents a quick setup that enables you to take fullest advantage of the ensemble capabilities of SAS Enterprise Miner by using existing nodes, Start Groups and End Groups nodes, and custom coding | macro definition, data mining | source | 15 |
| 193 | Leveraging Publicly Available Data in the Classroom Using SAS PROC SURVEYLOGISTIC | Tyler C. Smith, Besa Smith |
The soaring number of publicly available data sets across disciplines has allowed for increased access to real-life data for use in both research and educational settings. These data often leverage cost-effective complex sampling designs including stratification and clustering, which allow for increased efficiency in survey data collection and analyses. Weighting becomes a necessary component in these survey data in order to properly calculate variance estimates and arrive at sound inferences through statistical analysis. Generally speaking, these weights are included with the variables provided in the public use data, though an explanation for how and when to use these weights is often lacking. This paper presents an analysis using the California Health Interview Survey to compare weighted and non-weighted results using SAS PROC LOGISTIC and PROC SURVEYLOGISTIC |
proc step, logistic distribution, proc surveylogistic, logistic regression, proc logistic, sample, regression analysis, odds ratio, confidence interval, likelihood function, dataset, predictor variable | source | 18 |
| 194 | Leveraging SAS Visual Analytics for Healthcare Research | Al Cordoba, Jim Fenton, William Marder, Tony Pepitone |
There is increasing interest in exploring healthcare practices and costs for the US population to improve the quality, efficiency and outcomes of care. Fortunately, comparative data derived from inpatient, outpatient and prescription drug claims is available to evaluate and benchmark financial and clinical performance. The data source described in this paper represents the collective experience of tens of millions of people over many years. While it is highly useful to visualize trends and relationships in the data, this has always been difficult because of the scale. In this paper, the authors describe their experience extracting data from Truven Health MarketScan Research Databases, loading the data into SAS Visual Analytics and exploring the data to better understand drug adherence patterns for a population of diabetes patients | sas visual analytics, business intelligence | source | 9 |
| 195 | Log Entries, Events, Performance Measures, and SLAs: Understanding and Managing your SAS Deployment by Leveraging the SAS Environment Manager Data Mart | Bob Bonham, Gregory A. Smith |
SAS Environment Manager is included within the release of SAS 9.4. This exciting new product allows administrators to monitor the performance and operation of their SAS deployments. What very few people are aware of is that the data collected by SAS Environment Manager is stored in a centralized data mart that is designed to help administrators to better understand the behavior and performance of the components of their SAS solution stack. This data mart can also be used to help organizations meet their IT Infrastructure Library (ITIL) reporting and measurement requirements. In addition to the information about alerts, events and performance metrics collected by the SAS Environment Manager agent technology, this data mart includes the metadata audit and content usage data previously only available from the SAS Audit and Performance Measurement (APM) package | data mart, key performance indicator | source | 11 |
| 196 | Macro Design Ideas: Theory, Template, Practice | Ronald J. Fehd | macro function, full join, %if-%then-%else, macro variable, proc freq, macro loop | source | 21 | |
| 197 | Make It Possible: Create Customized Graphs with Graph Template Language | Wen Song, Ge Wu |
Effective graphs are indispensable for modern statistical analysis. They reveal tendency that are not readily apparent in simple tables and add visual clarity to reports. My client is a big graph fan; he always shows me a lot of high quality and complex sample graphs that were created by other software and asks me "Can SAS duplicate these outputs?" Often, by leveraging the capabilities of the ODS Graphics Template Language (GTL) and the SGRENDER procedure, the answer is "Yes". GTL offers SAS users more direct approach to customize the output and to overlay graphs in different level. This paper uses cases drawn from a real work situation to demonstrate how to get the seemingly unattainable results with the power of GTL: Utilizing bubble plots as your distribution density bars Creating refreshing looking linear regression graphics with the slop information in the legend * Overlaying different plots together to create sophisticated analytical bottleneck test output |
proc reg, regression analysis, sample | source | 6 |
| 198 | Make SAS Enterprise Guide Your Own | John F. Ladds | If you have been programming in Base SAS with SAS Display Manager for years, you will have made SAS Display Manager your own: customized window layout, customized program text colours, bookmarks and abbreviations/keyboard macros. Now you are using SAS Enterprise Guide. Did you know you can have almost all the same enhancements you had in SAS Display Manager in SAS Enterprise Guide plus more | sas enterprise guide, sas base | source | 13 |
| 199 | Making Comparisons Fair: How LS-Means Unify the Analysis of Linear Models | Weijie Cai | How do you compare group responses when the data are unbalanced or when covariates come into play? Simple averages will not do, but LS-means are just the ticket. Central to postfitting analysis in SAS/STAT linear modeling procedures, LS-means generalize the simple average for unbalanced data and complicated models. They play a key role both in standard treatment comparisons and Type III tests and in newer techniques such as sliced interaction effects and diffograms. This paper reviews the definition of LS-means, focusing on their interpretation as predicted population marginal means, and it illustrates their broad range of use with numerous examples |
sample, proc plm, proc step, least squares mean, logistic distribution | source | 22 |
| 200 | Making It Happen: A Novel Way to Combine, Construct, Customize, and Implement Your Big Data with a SAS Data Discovery, Analytics, and Fraud Framework in Medicare | Vivek Sethunatesan | A common complaint from business users working on identifying fraud and abuse in Medicare is that teams focus on operational applications, static reports, and high level outliers, but when faced with the need to constantly evaluate changing Medicare provider and beneficiary/enrollee dynamics, those business units clamor for more dynamic and accurate detection approaches. Providing these organizations with a data discovery and predictive analytics framework that leverages Hadoop and other big data analytical approaches, while providing a clear path for rapid fact-based decisions, is critical in pre- and post- payment fraud and abuse analysis. This paper describes the data management, data discovery, predictive analytics, and social network analysis capabilities that are included in the fraud framework, and how a unified approach can significantly reduce the analytical life cycle of building and deploying fraud models | case management, outlier, social network analysis, proc sql, data governance, predictive analytics, relational database, business intelligence, data profiling, sql pass-through, suspicious activity, claims fraud | source | 15 |
| 201 | Making the Log a Forethought Rather Than an Afterthought | Emmy V Pahmer | When we start programming, we simply hope that the log comes out with no errors or warnings. Yet once we have programmed for a while, especially in the area of pharmaceutical research, we realize that having a log with specific, useful information in it improves quality and accountability. We discuss clearing the log, sending the log to an output file, helpful information to put in the log, which messages are permissible, automated log checking, adding messages regarding data changes, whether or not we want to see source code, and a few other log-related ideas. Hopefully, the log will become something that we keep in mind from the moment we start programming | sas log, put statement, proc step, proc printto, options statement, sample | source | 10 |
| 202 | Managing Large Data with SAS Scalable Performance Data Server Cluster Table Transactions | Guy Simpson | proc step, proc sql | source | 12 | |
| 203 | Managing Opt-Out Risk | Jia Lei | Email is an important marketing channel for digital marketers. Marketers can stay connected with the subscribers and continue to engage them with relevant content as long as they are still subscribed to email communication. Every marketing email received can be an opportunity to transact for one user. At the same time, it can be an opportunity to unsubscribe for another user. Monitoring the unsubscribe activity is important as it speaks to the health of the email program. Is the content relevant? Are emails going out at the right time, right cadence? Even when unsubscribe rate is being maintained at a steady stage, knowing who are the users with high opt-out risk will allow marketers treat them a bit differently and keep them staying longer, thus optimizing the overall contact strategy | logistic distribution, proc freq, proc step, logistic regression, proc logistic, correlation, proc varclus, regression analysis, data cleaning | source | 4 |
| 204 | Managing the Data Governance Lifecyle | Scott Gidley | Data governance combines the disciplines of data quality, data management, data policy management, business process management, and risk management into a methodology that ensures that important data assets are formally managed throughout an enterprise. SAS has developed a cohesive suite of technologies that can be used to implement efficient and effective data governance initiatives, thereby improving an enterprise's overall data management efficiency. This paper discusses data governance use cases and challenges and provides an example of how to manage the data governance life cycle to ensure success | data governance, data cleaning | source | 7 |
| 205 | Managing the Organization of SAS Format and Macro Code Libraries in Complex Environments Including PC SAS, SAS Enterprise Guide, and UNIX SAS | Roger D. Muller | The capabilities of SAS have been extended by the use of macros and custom formats. SAS macro code libraries and custom format libraries can be stored in various locations, some of which may or may not always be easily and efficiently accessed from other operating environments. Code can be in various states of development ranging from global organization-wide approved libraries to very elementary "just-getting-started" code. Formalized yet flexible file structures for storing code are needed. SAS user environments range from standalone systems such as PC SAS or SAS on a server/mainframe to much more complex installations using multiple platforms. Strictest attention must be paid to (1) file location for macros and formats and (2) management of the lack of cross-platform portability of formats. Macros are relatively easy to run from their native locations | user-defined format, format statement, macro definition, proc format, proc step, sas enterprise guide | source | 9 |
| 206 | Matching Rules: Too Loose, Too Tight, or Just Right? | Richard Cadieux, Daniel R. Bretheim |
This paper describes a technique for calibrating street address match logic to maximize the match rate without introducing excessive erroneous matching | second, sample | source | 11 |
| 207 | Measures of Fit for Logistic Regression | Paul D. Allison | One of the most common questions about logistic regression is "How do I know if my model fits the data?" There are many approaches to answering this question, but they generally fall into two categories: measures of predictive power (like R-square) and goodness of fit tests (like the Pearson chi-square). This presentation looks first at R-square measures, arguing that the optional R-squares reported by PROC LOGISTIC might not be optimal. Measures proposed by McFadden and Tjur appear to be more attractive. As for goodness of fit, the popular Hosmer and Lemeshow test is shown to have some serious problems. Several alternatives are considered |
logistic distribution, logistic regression, regression analysis, proc logistic, null hypothesis, proc step, sample, response variable, likelihood function, standard deviation, sample size, degrees of freedom, proc reg, chi-square distribution, predictor variable, chi-square test, normal distribution, proc ttest, proc freq | source | 12 |
| 208 | Medical Scoring for Breast Cancer Recurrence | Nurul Husna Jamian, Yap Bee Wah, Nor Aina Emran |
Breast cancer is the most common cancer among females globally. After being diagnosed and treated for breast cancer, patients fear the recurrence of breast cancer. Breast cancer recurrence (BCR) can be defined as the return of breast cancer after primary treatment, and it can recur within the first three to five years. BCR studies have been conducted mostly in developed countries such as the United States, Japan, and Canada. Thus, the primary aim of this study is to investigate the feasibility of building a medical scorecard to assess the risk of BCR among Malaysian women. The medical scorecard was developed using data from 454 out of 1,149 patients who were diagnosed and underwent treatment at the Department of Surgery, Hospital Kuala Lumpur from 2006 until 2011. The outcome variable is a binary variable with two values: 1 (recurrence) and 0 (remission). Based on the availability of data, only 13 categorical predictors were identified and used in this study | logistic regression, regression analysis, logistic distribution, credit scoring, sample, roc curve, sample size, data cleaning, odds ratio, data mining, predictor variable, normality test | source | 13 |
| 209 | Migrating SAS Java EE Applications from WebLogic, WebSphere, and JBoss to Pivotal tc Server | Zhiyong Li, Alec S. Fernandez |
SAS has a large portfolio of Java EE applications. In releases previous to SAS 9.4, SAS provides support for configuring, deploying, and running these applications in Oracle WebLogic, IBM WebSphere, or Red Hat JBoss. Beginning with SAS 9.4, SAS has updated the middle-tier architecture to deliver and run these web applications exclusively in the SAS Web Application Server (a specialized, extended configuration of Pivotal tc Server) rather than the other third-party web application servers. This paper discusses the motivation, technology selections, and architecture on which this change is based. It also describes the advantages that the new approach presents to customers, including increased automation of installation and configuration tasks and improved system administration | data mining, sas management console, red hat | source | 12 |
| 210 | Missing Data: Overview, Likelihood, Weighted Estimating Equations, and Multiple Imputation | Geert Molenberghs | Presentation | multiple imputation, imputation, likelihood function | source | 56 |
| 211 | Mobile Reporting at University of Central Florida | Carlos Piemonti | Mobile devices are taking over conventional ways of sharing and presenting information in today's businesses and working environments. Accessibility to this information is a key factor for companies and institutions in order to reach wider audiences more efficiently. SAS software provides a powerful set of tools that allows developers to fulfill the increasing demand in mobile reporting without needing to upgrade to the latest version of the platform. Here at University of Central Florida (UCF), we were able to create reports targeting our iPad consumers at our executive level by using the SAS 9.2 Enterprise Business Intelligence environment, specifically SAS Web Report Studio 4.3. These reports provide them with the relevant data for their decision-making process | sas enterprise guide, excel | source | 6 |
| 212 | Model Variable Selection Using Bootstrap Decision Tree | David J. Corliss | Bootstrapped Decision Tree is a variable selection method used to identify and eliminate unintelligent variables from a large number of initial candidate variables. Candidates for subsequent modeling are identified by selecting variables consistently appearing at the top of decision trees created using a random sample of all possible modeling variables. The technique is best used to reduce hundreds of potential fields to a "short list" of 30 -50 fields to be used in developing a model. This method for variab le selection has recently become available in JMP under the name BootstrapForest; this paper presents an implementation in Base SAS9. The method does accept but does not require a specific outcome to be modeled and will therefore work for nearly any type of model, including segmentation, MCMC, multiple discrete choice, in addition to standard logistic regression. Keywords: Bootstrapped Decision Tree, Variable Selection | proc hpsplit, proc step, proc fastclus, feature selection, bootstrap, sample, sas base, numeric variable, proc contents, dataset, variable list, character variable, proc freq | source | 7 |
| 213 | Modeling Fractional Outcomes with SAS | Wensui Liu, Jason Xin |
For practitioners, OLS (Ordinary Least Squares) regression with Gaussian distributional assumption has been the top choice to model fractional outcomes in many business problems. However, it is conceptually flawed to assume Gaussian distribution for a response variable in the [0, 1] range. In this paper, several modeling techniques for fractional outcomes with their implementations in SAS should b e discussed through a data analysis exercise in modeling financial leverage ratios of businesses. The purpose of this paper is to provide a relatively comprehensive survey of how to model fractional outcomes to the SAS user community and interested statistical practitioners. Keywords Fractional outcomes, Tobit model, NLS ( Non-linear Least Squares) regression, Fractional Logit model, Beta regression, Simplex regression, Vuong statistic | regression analysis, logistic regression, likelihood function, estimate, normal distribution, proc nlmixed, sample, logistic distribution, model training, proc step, probability functions | source | 17 |
| 214 | Modeling Loss Given Default in SAS/STAT | Xiao Yao, Jonathan Crook, Galina Andreeva |
Predicting loss given default (LGD) is playing an increasingly crucial role in quantitative credit risk modeling. In this paper, we propose to apply mixed effe cts models to predict corporate bonds LGD, as well as other widely used LGD models. The empirical results show that mixed effects models are able to explain the unobservable heterogeneity and to make better predictions compared with linear regression and fractional res ponse regression. All the statistical models are performed in SAS/STAT, SAS 9.2, usin g specifically PROC REG and PROC NLMIXED, and the model evaluation metrics are calculated in PROC IML. This paper gives a detailed description on how to use PROC NLMIXED to build and estimate generalized linear models and mixed effects models |
regression analysis, recovery rate, proc step, proc mixed, proc reg, likelihood function, proc iml, correlation, key performance indicator, response variable, sample, fixed effects, lgd, beta distribution, out=, covariance | source | 9 |
| 215 | Modeling Ordinal Responses for a Better Understanding of Drivers of Customer Satisfaction | Jorge Alejandro, Sharon Kim |
While survey researchers make great attempts to standardize their questionnaires - including the usage of ratings scales - oftentimes the methods applied rely on the assumption that the collected response is interval level, as opposed to ordinal. Although analytical approaches that are better equipped to handle ordinal responses have become more accessible over the last several years, many times their use is affected by the requirements of the method, including sample size. Furthermore, often there are sub-populations of interest that are left unexplored because of the additional strain this would impose on the sample size requirement. This paper describes an implementation that uses a Bayesian approach to model an ordinal response while capturing the distribution of heterogeneity in the included sub-populations. More specifically, SAS PROC MCMC is used to carry out a comprehensive modeling strategy of ratings data that accounts for the use of an ordinal response scale |
customer satisfaction, markov chain, sample size, proc step, sample, monte carlo | source | 8 |
| 216 | Modernizing Your Data Strategy: Understanding SAS Solutions for Data Integration, Data Quality, Data Governance, and Master Data Management | Gregory S. Barnes Nelson, Lisa Dodson |
For over three decades, SAS has provided capabilities for beating your data into submission. In June of 2000, SAS acquired a company called DataFlux to add data quality capabilities to its portfolio. Recently, SAS folded Data Flux into the mother ship and with SAS 9.4, the SAS Enterprise Data Integration (and baby brother Data Integration) solutions were upgraded into a series of new bundles that still include the former DataFlux products, but those products have grown. These new bundles include data management, data governance, data quality and master data management and come in advanced and standard packaging. This paper will explore these offerings and help you understand what this means to both new and existing customers of the Data Integration and DataFlux products. We will break down the marketing jargon and give you real world scenarios of what customers are using today (pre-SAS 9.4) and walk you through what that might look like in the SAS 9.4 world | data governance, master data management | source | 18 |
| 217 | Money Basketball: Optimizing Basketball Player Selection Using SAS | Sabah Sadiq, Jing Zhao |
Over the past decade, sports analytics has seen an explosion in research and model development to calculate wins, reaching cult popularity with the release of Moneyball. Drawing inspiration from the Maximum Coverage Problem (MCP) in Operations Research, we will explore the methodology and possible solutions to a real-life "Moneyball" problem in basketball drafting. We aim to determine a player's worth through clustering their shots together into "sweet spots" and estimating the number of shots per game within each sweet spot. This data will then be fed into an optimization algorithm to select an optimal basketball lineup that maximizes total field goals per game along with court coverage. Finally, additional constraints will be added into the model that solve for three different scenarios | raw data, proc step | source | 9 |
| 218 | More Than a Map: Location Intelligence with SAS Visual Analytics | Falko Schulz, Anand V. Chitale |
More organizations are understanding the importance of geo-tagged data and the need for tools that can successfully combine location data with business metrics to provide intelligent outputs that are beyond a simple map. SAS Visual Analytics provides a robust and powerful platform for achieving location intelligence performed with a combination of SAS Analytics and GIS mapping technologies such as that offered by Esri. This paper describes the essentials for achieving location intelligence and demonstrates with industry examples how SAS Visual Analytics makes it possible | location intelligence, bar chart | source | 12 |
| 219 | Multivariate Ratio and Regression Estimators | Alan Ricardo da Silva | This paper considers showing %mre macro to estimate multivariate ratio estimates. Al so, we can use PROC REG to estimate multivariate regression estimates and to show that regression estimates are superior to the ratio estimates |
regression analysis, correlation, proc reg, sample, macro call, random sampling | source | 13 |
| 220 | Multivariate Time Series Modeling Using PROC VARMAX | Anders Milhøj | Two examples of Vector Autoregressive Moving Average modeling with exogenous variables are given in this presentation. Data is from the real world. One example is about a two-dimensional time series for wages and prices in Denmark that spans more than a hundred years. The other is about the market for agricultural products, especially eggs! These examples give a general overview of the many possibilities offered by PROC VARMAX, such as handling of seasonality, causality testing and Bayesian modeling, and so on |
autoregressive model, proc varmax, time series analysis, proc step, prior probability, residual, correlation | source | 16 |
| 221 | Navigate the SAS Log - GPS Style | Ethan Miller, Rebecca Ottesen |
Sometimes the notes, warnings and errors in the SAS Log can be cryptic at best. Hours of programming and deciphering the log can make a person feel a little down and somewhat nutty. What if there was a way to have the SAS log be informative and amusing at the same time? Having the option to change how the SAS log communicates might actually keep a user from throwing their computer out the window. Our aim is to help thousands of SAS programmers to understand how the messages in the log can be interpreted in an entertaining way | sas log, missing value, error message, numeric variable, macro definition | source | 11 |
| 222 | Near Time, Real Time&Big; Time | Michael Ralston | Greater data availability leads to potential greater depth and subtlety of modeling, but building a model and gaining actionable business insight from analytic data is fundamentally a fixed process (there are no short cuts). There are different impacts however. Big Data Analytic Processing taxes the process in one way, while Analytic Exploration taxes it in another. The process can certainly be optimized, streamlined, and supercharged - but only by understanding the strengths and interactions of the tools and techniques available, and their impact on the overall value. A great recipe, with better tools or more ingredients won't improve a meal that isn't served while it's still hot. Big Data Analytics is essentially mass production analysis - focus on optimizing the process and the value will emerge. Get it while it's hot! | data science, business intelligence | source | 10 |
| 223 | New Features in SAS/OR 13.1 | Edward P. Hughes, Robert C. Pratt |
SAS/OR software for operations research includes mathematical optimization, discrete-event simulation, and project and resource scheduling capabilities. This paper surveys a number of its new features that better equip you to address decision-making challenges such as planning, resource management, and asset allocation. Optimization performance improvements help you solve larger, more detailed problems more quickly. Improvements encompass linear, mixed integer linear, and nonlinear optimization, and include multithreading of the mixed integer linear solver and major improvements in the performance and functionality of the decomposition algorithm for linear and mixed integer linear optimization. The OPTMODEL procedure for optimization modeling adds direct access to the same set of efficient network optimization algorithms available via the OPTNET procedure in SAS/OR, enabling you to embed network optimization as a component of larger solution processes |
proc step, odds ratio, proc fcmp, proc optlso, parallel processing, cpu time, network optimization | source | 15 |
| 224 | NIHB Pharmacy Surveillance System | Jeffrey Menzies, Ian Ghent |
This paper provides insight from implementation leads from SAS Professional Services and Health Canada's Non- Insured Health benefits (NIHB) program, on a joint implementation of SAS Fraud Framework for Health Care. We will walk through the fast paced implementation of NIHB's Pharmacy Surveillance System that guards Canadian taxpayers from undue costs, and protects the safety of NIHB clients. We will blend project management and technical material from both the client (NIHB) and consultant (SAS) perspectives throughout the story. We conclude with several core principles needed to successfully deliver analytical solutions | service provider, data cleaning | source | 9 |
| 225 | Nitty Gritty Data Set Attributes | Diane Olson | Most programmers are familiar with the directive, "Know your data." But not everyone knows about all the metadata stored in a SAS data set or understands what to do with this information. This paper covers the majority of these attributes, how to obtain them, why they are important, and what you can do with them. For example, data sets that have been around for a while might have an inordinate number of deleted observations that you are carrying around unnecessarily. Or you might be able to quickly check to determine whether the data set is indexed-and if so, by what variables-to increase your program's performance. Also, engine-dependent data such as owner name and file size is found in PROC CONTENTS output, which is useful for understanding and managing your data. You can also use ODS OUTPUT to use the values of these many attributes programmatically. This paper shows you how |
proc step, proc contents, constraint, sas base, audit trail, options statement, proc copy, ods output, output, data integrity, libname statement, dataset | source | 11 |
| 226 | Non-Empirical Modeling: Incorporating Expert Judgment as a Model Input | John Parker | Presentation | monte carlo, standard deviation, regression analysis, bootstrap, correlation | source | 20 |
| 227 | Nonnegative Least Squares Regression in SAS | Matthew Duchnowski | It is often the case that parameters in a predictive model need to be restricted to an interval that is either reasonable or necessary given the model's application. A simple and classic example of such a restriction is the regression model which requires that all parameters be positive. In the case of multiple linear least squares regression (MLR), the resulting model is therefore strictly additive and, in certain applications, not only appropriate but also intuitive. This special case is commonly referred to as a "nonnegative least squares" (NNLS) regression. While Base SAS contains a multitude of ways to perform a least squares regression (PROC REG and PROC GLM, to name two), there exists no native SAS procedure to conduct a nonnegative least squares regression. The author offers a concise way to conduct the nonnegative least squares analysis by using PROC NLIN (PROC "nonlinear"). PROC NLIN offers parameter restriction to the user |
proc step, proc nlin, proc reg, estimate, regression analysis, macro variable, predictor variable, out= | source | 5 |
| 228 | ODBC Connection to a Database Using Keywords and SAS Macros | Jesper Michelsen | This ePoster paper will show the audience how to connect to a database without any registration of the ODBC connection in neither the Windows ODBC Administrator tool nor in the Windows Registry database. Furthermore, it is shown how the connection can be more flexible and better managed by building it into a SAS macro | odbc connection, macro variable, %if-%then-%else | source | 3 |
| 229 | OLAP Drill-through Table Considerations | Michelle Buchecker | When creating an OLAP cube, you have the option of specifying a drill-through table, also known as a Show Details table. This quick tip discusses the implications of using your detail table as your drill-through table and explores some viable alternatives | star schema, dimension table, where statement | source | 5 |
| 230 | Ordering Columns in a SAS Data Set: Should You Really RETAIN That? | Andrew W. Clapson | When viewing and working with SAS data sets - especially wide ones - it's often instinctive to rearrange the variables (columns) into some intuitive order. The RETAIN statement is one of the most commonly cited methods used for ordering variables. Though RETAIN can perform this task, its use as an ordering clause can cause a host of easily missed problems due to its intended function of retaining values across DATA STEP iterations. This risk is especially great for the more novice SAS programmer. Instead, two equally effective and less risky ways to order data set variables are recommended, namely, the FORMAT and SQL SELECT statement |
retain, select, set statement, proc sql | source | 3 |
| 231 | P-values: Democrats or Dictators? | Brenda Beaty, Michelle Torok |
Part of being a good analyst and statistician is being able to understand the output of a statistical test in SAS. P- values are ubiquitous in statistical output as well as medical literature and can be the deciding factor in whether a paper gets published. This shows a somewhat dictatorial side of them. But do we really know what they mean? In a democratic process, people vote for another person to represent them, their values, and their opinions. In this sense, the sample of research subjects, their characteristics, and their experience, are combined and "represented" to a certain degree by the p-value. This paper discusses misconceptions about and misinterpretations of the p-value, as well as how things can go awry in calculating a p-value. Alternatives to p-values are described, with advantages and disadvantages of each. Finally, some thoughts about p-value interpretation are given | null hypothesis, confidence interval, sample, p-value, sample size | source | 4 |
| 232 | Parallel Data Preparation with the DS2 Programming Language | Jason Secosky, Robert S. Ray, Greg Otto |
A time-consuming part of statistical analysis is building an analytic table for statistical procedures. Whether it is recoding input values, transforming variables, or combining data from multiple data sources, the work to create an analytic table can take time. The DS2 programming language in SAS 9.4 simplifies and speeds data preparation with user-defined methods, storing methods and attributes in shareable packages, and threaded execution on multi- core symmetric multiprocessing (SMP) and massively parallel processing (MPP) machines. Come see how DS2 makes your job easier | by group processing, proc sql, parallel processing, data wrangling, proc step, elapsed time, class variable, teradata | source | 11 |
| 233 | Parameter Estimation of Cognitive Attributes Using the Crossed Random-Effects Linear Logistic Test Model with PROC GLIMMIX | Chunhua Cao, Yan Wang, Yi-Hsin Chen, Isaac Y. Li |
A simulation study is designed to explore accuracy and precision of parameter estimation of cognitive attributes using the crossed random effects linear logistic test model (CRELLTM) with PROC GLIMMIX. The CRELLTM characterizes the weight of the cognitive attributes for item difficulties as random coefficients. The SAS GLIMMIX package is applied to obtain parameter estimates. A series of simulations are conducted. Manipulated factors in this study include the sample size, the number of items, the population distribution shape, and the Q-matrix density. The number of cognitive attributes is fixed as 8 attributes. The results indicate that the CRELLTM using the SAS GLIMMIX procedure yields negligible bias and high correlations between estimated and true cognitive attributes; however, the RMSE is relatively large, especially with small sample size (i.e., 25 in this study) |
sample size, sample, correlation, proc glimmix, effect size, proc step, proc mixed, factorial anova, estimate, anova, main effect, logistic distribution | source | 12 |
| 234 | PD_Calibrate Macro | Keld Asnæs, Jesper Michelsen |
The Model team at Nykredit Group Risk Management has standardized the calibration process of the credit-score models developed. This ePoster and paper gives a brief summary of some central elements and the use of the macro | default rate, proc append | source | 2 |
| 235 | PDF vs HTML: Can't We All Just Get Along? | Scott Huntley, Cynthia L. Zender |
Have you ever asked, "Why doesn't my PDF output look just like my HTML output?" This paper explains the power and differences of each destination. You will learn how each destination works and understand why the output looks the way it does. Learn tips and tricks for how to modify your SAS code to make each destination look more like the other. The tips span from beginner to advanced in all areas of reporting. Each destination is like a superhero, helping you transform your reports to meet all your needs. Learn how to use each ODS destination to the fullest extent of its powers |
ods pdf, proc step, proc report, ods html, proc tabulate, proc print, output delivery system, by group processing | source | 33 |
| 236 | Plotting Against Cancer: Creating Oncology Plots Using SAS | Debpriya Sarkar | Graphs in oncology studies are essential for getting more insight about the clinical data. This presentation demonstrates how ODS Graphics can be effectively and easily used to create graphs used in oncology studies. We discuss some examples and illustrate how to create plots like drug concentration versus time plots, waterfall charts, comparative survival plots, and other graphs using Graph Template Language and the ODS Graphics procedures. These can be easily incorporated into a clinical report |
proc sgplot, heatmap, waterfall chart, proc sgpanel, graph template language, correlation, proc step, bar chart, confidence interval, do loop, hazard ratio, clinical trial, odds ratio, proc template | source | 30 |
| 237 | Plotting Differences Among LS-means in Generalized Linear Models | Robin High | The effectiveness of visual interpretation of the differences between pairs of LsMeans in a generalized linear model includes the graph's ability to display four inferential and two perceptual tasks. Among the types of graphs which display some or all of these tasks are the forest plot, the mean-mean scatter plot (diffogram), and the mean-mean multiple comparison (MMC) plot. These graphs provide essential visual perspectives for interpretation of the differences among pairs of LsMeans from a Generalized Linear Model (GLM). The diffogram is a graphical option now available through ODS statistical graphics with linear model procedures such as GLIMMIX of SAS. Through combining ODS output files of the lsmeans and their differences, the SGPLOT procedure can efficiently produce forest and MMC plots. Visual Displays Visual displays of distributions are important aids to compare variation in data |
confidence interval, forest plot, data visualization, sample, anova, proc step, sample size, scatter plot, repeated measures, proc glimmix, standard deviation, proc sql, proc mixed, proc sgplot, proc ttest | source | 14 |
| 238 | Power and Sample Size for MANOVA and Repeated Measures with the GLMPOWER Procedure | John M. Castelloe | Power analysis helps you plan a study that has a controlled probability of detecting a meaningful effect, giving you conclusive results with maximum efficiency. SAS/STAT provides two procedures for performing sample size and power computations: the POWER procedure provides analyses for a wide variety of different statistical tests, and the GLMPOWER procedure focuses on power analysis for general linear models. In SAS/STAT 13.1, the GLMPOWER procedure has been updated to enable power analysis for multivariate linear models and repeated measures studies. Much of the syntax is similar to the syntax of the GLM procedure, including both the new MANOVA and REPEATED statements and the existing MODEL and CONTRAST statements. In addition, PROC GLMPOWER offers flexible yet parsimonious options for specifying the covariance |
correlation, sample size, power analysis, sample, proc step, proc glmpower, proc mixed, standard deviation, f-test, response variable, repeated measures, within-subject correlation, proc genmod, covariance, proc glm | source | 20 |
| 239 | Powerful and Hard-to-find PROC SQL Features | Kirk Paul Lafler | The SQL procedure contains many powerful and elegant language features for intermediate and advanced SQL users. This presentation discusses topics that will help SAS users unlock the many powerful features, options, and other gems found in the SQL universe. Topics include CASE logic; a sampling of summary (statistical) functions; dictionary tables; PROC SQL and the SAS macro language interface; joins and join algorithms; PROC SQL statement options _METHOD, MAGIC=101, MAGIC=102, and MAGIC=103; and "key" performance (optimization) issues |
proc sql, proc step, dictionary tables, where statement, sas log, select, macro variable, inner join, sas library, macro facility, macro call, order by, right join, view, data merging | source | 19 |
| 240 | Predicting a Child Smoker Using SAS Enterprise Miner 12.1 | Jin Ho Jung, Gaurav Pathak, Goutam Chakraborty |
Presentation | regression analysis, logistic regression, data mining, logistic distribution | source | 3 |
| 241 | Prediction of the Rise in Sea Level by the Memory-Based Reasoning Model Using SAS Enterprise Miner 12.1 | Prasanna K S Sailaja Bhamidi, Goutam Chakraborty |
Presentation | proc hpsplit, roc curve | source | 4 |
| 242 | Predictive Analytics Reporting Framework | Heidi Hiemstra | Presentation | time series analysis, proc freq | source | 6 |
| 243 | Prescription for Visualization: Take One SAS Graph Template Language Graph before Seeing the Patient | Radhikha Myneni, Eric C. Brinsfield |
The steady expansion of electronic health record (EHR) systems over the past decade has increased the use of observational healthcare data for analysis. One of the challenges with EHR data is to combine information from different domains (diagnosis, procedures, drugs, adverse events, labs, quality of life scores, and so on) onto a single timeline to get a longitudinal view of the patient. This enables the physician or researcher to visualize a patient's health profile, thereby revealing anomalies, trends, and responses graphically, thus empowering them to treat more effectively. This paper attempts to provide a composite view of a patient using SAS Graph Template Language (GTL) to create a profile graph using the following data elements: key event dates, drugs, adverse events, and Quality of Life (QoL) scores | adverse events, graph template language, sas base, series plot, sample, proc step | source | 9 |
| 244 | PROC STREAM and SAS Server Pages: Generating Custom HTML Reports | Donald J. Henderson | ODS is a power tool for generating HTML based reports. Quite often however there are exacting requirements for report content, layout and placement that can be done with HTML (and especially HTML5) that can't be done with |
proc step, macro variable, macro facility, sample, macro definition, macro call, macro function, dataset, macro loop, do loop, &variable, proc sql, dosubl, sas enterprise guide, %if-%then-%else | source | 18 |
| 245 | PROC STREAM and SAS Server Pages: Generating Custom User Interfaces | Donald J. Henderson | Quite often when building web applications that use either the SAS Stored Process Server or the SAS/INTRNET Applications Dispatcher it is necessary to create a custom user interface to prompt for the needed parameters. Generating a custom user interface can be accomplished by chaining, for example, two or more stored processes together. The first stored process generates the user interface where the user selects the desired options and can use PROC STREAM to process an input SAS Server Page to display the user interface. The second (or later) stored process in the chain generates the desired output. This paper will describe and show several examples similar to those shown in the SAS Press book SAS Server Pages: Generating Dynamic Contact and on Don Henderson's blog Jurassic SAS in the BI/EBI World" |
proc step, macro variable, proc sql, dataset, dosubl, macro facility, sample, macro definition | source | 13 |
| 246 | PROC TABULATE: Extending This Powerful Tool Beyond Its Limitations | Justin Jia, Amanda Lin |
PROC TABULATE is a powerful tool for creating tabular summary reports. Its advantages, over PROC REPORT, are that it requires less code, allows for more convenient table construction, and it uses syntax that makes it easier to modify a table's structure. However, its inability to compute the sum, difference, product and ratio of column sums has hindered its use in many circumstances. This paper will illustrate and discuss some creative approaches and methods for overcoming these limitations, which allow users to produce needed reports and still enjoy PROC TABULATE's simplicity and convenience. These methods and skills can have prominent applications in a variety of marketing research, business intelligence and analytics fields |
proc step, proc tabulate, proc report, raw data, excel, cpu time, missing value, class variable, proc print | source | 11 |
| 247 | Processing and Storing Sparse Data in SAS Using SAS Text Miner Procedures | Zheng Zhao, Russell Albright, James A. Cox |
Sparse data sets are common in applications of text and data mining, social network analysis, and recommendation systems. In SAS software, sparse data sets are usually stored in the coordinate list (COO) transactional format. Two major drawbacks are associated with this s parse data representation: First, most SAS procedures are designed to handle dense data and cannot consume data that are stored transactionally. In that case, the options for analysis are significantly limited. Second, a sparse data set in transactional f ormat is hard to store and process in distributed sys- tems. Most techniques require that all transactions for a particular object be kept together; this assumption is violated when the transactions of that object are distributed to different nodes of the g rid. This paper presents some different ideas about how to package all transactions of an object into a single row | proc sql, macro variable | source | 13 |
| 248 | Productionalizing SAS for Enterprise Efficiency At Kaiser Permanente | Ryan Henderson, Karl Petith |
Presentation | sample, sas enterprise guide | source | 6 |
| 249 | Programmatic Challenges of Dose Tapering Using SAS | Iuliana Barbalau, Chen Shi, Yang Yang |
In a good clinical study, statisticians and various s takeholders are interested in assessing and isolating the effect of non-study drugs. One common practice in clinical trials is that clinical investigators follow the protocol to taper certain concomitant medications in an attempt to prevent or resolve adve rse reactions and/or to minimize the number of subject withdrawals due to lack of efficacy or adverse event. To assess the impact of those tapering medicines during study is of high interest to clinical scientists and the study statistician. This paper pre sents the challenges and caveats of assessing the impact of tapering a certain type of concomitant medications using SAS v9.3 based on a hypothetical case. The paper also presents the advantages of visual graphs in facilitating communications between clinical scientists and the study statistician | concomitant medications, clinical trial, proc freq, sample, study day | source | 9 |
| 250 | Programming in a Distributed Data Network Environment: A Perspective from the Mini-Sentinel Pilot Project | Jennifer R. Popovic | Multi-site health science-related distributed data networks are becoming increasingly popular, particularly at a time where 'big data' and privacy are often competing priorities. Distributed data networks allow individual-level data to remain behind the firewall of the data holder, permitting the secure execution of queries against those local data and the return of aggregated data produced from those queries to the requester. These networks allow the use of multiple, varied sources of data for study purposes ranging from public health surveillance to comparative effectiveness research, without compromising data holders' concerns surrounding data security, patient privacy or proprietary interests | macro variable, dataset, grid computing, sas programming | source | 10 |
| 251 | Programming With CLASS: Keeping Your Options Open | Arthur L. Carpenter | Many SAS procedures utilize classification variables when they are processing the data. These variables control how the PROCEDURE FORMS groupings, summarizations, and analysis elements. For statistics procedures they are often used in the formation of the statistical model that is being analyzed. Classification variables may be explicitly specified with a CLASS statement, or they may be specified implicitly from their usage in the procedure. Because classification variables have such a heavy influence on the outcome of so many procedures, it is essential that the analyst have a good understanding of how classification variables are applied. Certainly there are a number of options (system and procedural) that affect how classification variables behave. While you may be aware of some of these options, a great many are new, and some of these new options and techniques are especially powerful. You really need to be open to learning how to program with CLASS |
proc step, missing value, proc freq, proc report, proc means, proc tabulate | source | 15 |
| 252 | Putting on the Ritz: New Ways to Style Your ODS Graphics to the Max | Dan Heath | Do you find it difficult to dress up your graphs for your reports or presentations? In SAS 9.4, w e have introduced new capabilities in the ODS Graphics system that give you the ability to style your graphs w ithout creating or modifying ODS styles. Some of the new capabilities include the follow ing: a new option to control the behavior of how ODS styles are applied graph syntax to override ODS style attributes for grouped plots enhanced attribute map support the ability to define font glyphs and images as plot markers In this presentation, w e w ill discuss these new features in detail, show ing examples in both a Graph Template Language (GTL) context and a statistical graphics ( SG) procedure context |
scatter plot, proc step, class variable, line chart, interval variable, proc sgplot | source | 15 |
| 253 | Queues for Newbies . How to Speak LSF in a SAS World | Andrew Howell | Wouldn't it be great to never have to wait in line at the bank, the airport, the hospital waiting room? The same applies in the SAS world, running SAS jobs (although I will return to the airport analogy in later scenarios). Ideally every SAS user has dedicated resources available on demand & scalable to process their task as near to real- time as possible. One way to achieve this is for every user to have their own dedicated PC (or server), cores, memory, etc. However, like all options, this has its drawbacks - for a large number of users, it's expensive, difficult to manage, but above from a technology perspective there would be a lot of wasted utilisation, with most machines only operating a fraction of the time. These days there is pressure to centralise processing - in an effort to reign in IT costs, simplify management, apply governance. But for a server to have enough "grunt" to be able to process requests on demand means either an overpowered server, or contention for resources | sas grid, sas management console, sas enterprise guide, data mining | source | 9 |
| 254 | Quick Hits - My Favorite SAS Tricks | Marje Fecht | Are you time-poor and code-heavy? It's easy to get into a rut with your SAS code and it can be time-consuming to spend your time learning and implementing improved techniques. This presentation is designed to share quick improvements that take 5 minutes to learn and about the same time to implement. The quick hits are applicable across versions of SAS and require only BASE SAS knowledge. Included are: - simple macro tricks - little known functions that get rid of messy coding - dynamic conditional logic - data summarization tips to reduce data and processing - generation data sets to impr ove data access and rollback | macro variable, macro function, sysdate9, proc step, proc sql, date variable, sas function, sas programmer, missing value, character functions, macro loop | source | 13 |
| 255 | Reading and Writing ZIP Files with SAS | Richard D. Langston | The ZIP access method is new with SAS 9.4. This paper provides several examples of reading from and writing to ZIP files using this access method, including the use of the DATA STEP directory management macros and the new MEMVAR= option |
filename statement, infile statement, sas log | source | 5 |
| 256 | Reading In Data Directly from Microsoft Word Questionnaire Forms | Sijian Zhang | If someone comes to you with hundreds of questionnaire forms in Microsoft (MS) Word file format, and asks you to subtract the data from the forms into a SAS dataset, you may have several ways to handle this. However, as a SAS programmer, the shortcut is to write a SAS program to read in the data directly from Word files into a SAS dataset. This paper will show how it can be done with simple SAS programming skills, such as using FILENAME with the PIPE option, DDE, function call EXECUTE( ), and so on |
dataset, call execute, sas programming | source | 7 |
| 257 | Real-Time Market Monitoring using SAS BI Tools | Amol Deshmukh, Jeff McDonald |
The Department of Market Monitoring at California ISO is responsible for promoting a robust, competitive, and nondiscriminatory electric power market in California by keeping a close watch on the efficiency and effectiveness of the ancillary service, congestion management, and real--time spot markets. We monitor the potential of market participants to exercise undue market power, the behavior of market participants that is consistent with attempts to exercise market power and the market performance that results from the interaction of market structure with participant behavior. In order to perform Monitoring activities effectively, DMM collects available data, designs, and implement reporting dashboards that track key market metrics. We are using various SASBI tools to develop and employ metrics and analytic tools applicable to market structure, participant behavior, and market performance | data mart, sas enterprise guide, excel, raw data | source | 10 |
| 258 | Recommending News Articles Using Cosine Similarity Function | Rajendra Ledalla Venkata Naga, Qing Wang, John Dilip Raj |
Predicting news articles customers are likely to view/read next provides a distinct advantage to news sites and Collaborative filtering is a widely used technique for the same. This paper details an approach within Collaborative Filtering that uses the 'Cosine Similarity Function' to achieve this purpose. The paper fur ther details two different approaches, customized targeting and article level targeting that can be used in marketing campaigns | proc iml, proc step | source | 7 |
| 259 | Recommending TV Programs Using Correlation | John Dilip Raj, Ledalla Venkata Naga Rajendra, Qing Wang |
Personalized recommender systems are being used in many industries to increase customer engagement. In the TV industry, this is primarily used to increase viewership , which in turn increases market share, revenue and profit . This paper attempts to develop a recommender system using the CORRELATION procedure under Collaborative Filtering Methodology. The only data requirement for this recommendation system would be past viewership of customers for a given time period |
proc corr, recommendation engine, correlation, proc step | source | 6 |
| 260 | Reducing Medical Costs Using a System Based on SAS for Provider and Facility Efficiency Scoring | Stephanie Poley | Healthcare expenditure growth continues to be a prominent healthcare policy issue, and the uncertain impact of the Affordable Care Act (ACA) has put increased pressure on payers to find ways to exercise control over costs. Fueled by provider performance analytics, Blue Cross and Blue Shield of North Carolina (BCBSNC) has developed innovative strategies that recognize, incentivize, and assist providers delivering high-quality and efficient care. A leading BCBSNC strategy has been the introduction of a tiered network product called Blue Select, which was launched in 2013 and is featured in the Health Insurance Marketplace. Blue Select is a PPO plan with differential member cost-sharing for tiered providers. Tier status of providers is determined by comparing providers to their peers on the efficiency and quality of the care they deliver | confidence interval, binomial distribution | source | 6 |
| 261 | Reevaluating Policy and Claims Analytics: A Case of Corporate Fleet and Non-Fleet Customers in the Automobile Insurance Industry | Kittipong Trongsawad, Jongsawas Chongwatpol |
Analyzing automobile policies and claims is an ongoing area of interest to the insurance industry. Although there have been many data mining projects in insurance sector over the past decade, the following questions - How can insurance firms retain their best customers? Will this damaged car be covered and get claim payment? How much of loss of claims associated with this policy will be? - do remain as common. This study applies data mining techniques using SAS Enterprise MinerTM to enhance insurance policies and claims. The main focus is on assessing how corporate fleet customers' policy characteristic and claim behavior are different from that of non-fleet customers. We believe that implementing advanced analytics can help create better planning for policy and claim management strategy | auto insurance, data mining, proc hpsplit, insurer, response variable, type ii error, regression analysis, stepwise selection | source | 10 |
| 262 | Regression Analysis of Duration and Severity Data: New Capabilities with SAS Software | Joseph C. Gardiner | Duration and severity data arise in several fields including biostatistics, demography, economics, engineering and sociology. SAS procedures LIFETEST, LIFEREG and PHREG are the workhorse for analysis of time to event data in applications in biostatistics. Similar methods apply to the magnitude or severity of a random event, where the outcome might be right, left or interval censored and/or, right or left truncated. All combinations of types of censoring and truncation could be present in the data set. Regression models such as the accelerated failure time model, the Cox model and the non-homogeneous Poisson model have extensions to address time-varying covariates in the analysis of clustered outcomes, multivariate outcomes of mixed types, and recurrent events | likelihood function, proc step, proc phreg, right censoring, maximum likelihood estimation, poisson distribution, hazard function, confidence interval, proc calis, time-dependent covariate, hazard ratio, accelerated failure time, sample, logistic distribution, regression analysis | source | 19 |
| 263 | Reporting Healthcare Data: Understanding Rates and Adjustments | Gregory S. Barnes Nelson | In healthcare, we often express our analytics results as being "adjusted". For example, you may have read a study in which the authors reported the data as "age-adjusted" or "risk-adjusted." The concept of adjustment is widely used in program evaluation, comparing quality indicators across providers and systems, forecasting incidence rates and in cost-effectiveness research. In order to make reasonable comparisons across time, place or population, need to account for small sample sizes and case-mix variation - in other words - we need to level the playing field and account for differences in health status and accounts for uniqueness in a given population. If you are new to healthcare it may not be obvious what that really means to adjust the data in order to make comparisons. In this paper, we will explore the methods by which we control for potentially confounding variables in our data | health insurance, sample size | source | 22 |
| 264 | Researching Individual Credit Rating Models | Colin Nugteren | In t his research paper we try to find an optimal model for predicting credit risk in a consumer - factoring environment. This is done for DirectPay and uses their data and knowledge. Both the possibilities of finding more and better data and using more intelligent models are explored. In the end the effect of better and more relevant data has the largest impact on the forecasting performance, but using more intelligent models can create an extra advancement | regression analysis, proc hpsplit, logistic regression, logistic distribution, credit card, data mining, expected loss, correlation | source | 12 |
| 265 | Revealing Human Mobility Behavior and Trips Prediction Based on Mobile Data Records | Carlos Andre R. Pinheiro | This paper reveals the human mobility behavior in the metropolitan area of Rio de Janeiro, Brazil. The base for this study is the mobile phone data provided by one of the largest mobile carriers in Brazil. Mobile phone data comprises a reasonable variety of information, including data about time and location for call activity throughout urban areas. This information might be used to build users' trajectories over the time, describing the major characteristics of the urban mobility within the city. A variety of distribution analyses is presented in this paper aiming clearly describes the most relevant characteristics of the overall mobility in the metropolitan area of Rio de Janeiro. In addition to that, methods from physics to describe trends in trips such as gravity and radiation models were computed and compared in terms of granularity of the geographic scales and also in relation to traditional data mining approach such as linear regressions | regression analysis, proc reg, simple linear regression, proc quantreg, proc robustreg, correlation, response variable, cdr | source | 14 |
| 266 | Revealing Unwarranted Access to Sensitive Data: A Scenario-based Approach | Heidi Thorstensen, Torulf Mollestad |
The project focuses on using analytics to reveal unwarranted use of access to medical records, i.e. employees in health organizations that access information about neighbours, friends, celebrities, etc., without a sound reason to do so. The method is based on the natural assumption that the vast majority of lookups are legitimate - lookups that differ from a statistically defined normal behavior will be subject to manual investigation. The work was carried out in collaboration between SAS Institute Norway and the largest Norwegian hospital, Oslo University Hospital (OUS) and was aimed at establishing whether the method is suitable for unveiling unwarranted lookups in medical records. A number of so called scenarios are used to indicate adverse behaviour, each responsible for looking at one particular aspect of journal access data | source | 6 | |
| 267 | Risk Factors and Outcome of Spinal Epidural Abscess from Incident Hemodialysis Patients from the United States Renal Data System between 2005 and 2008 | Chan Jin, Jennifer White, Rhonda Colombo, Stephanie Baer, Usman Afza, M. Kheda, Lu Huber, Puja Chebrolu, N. Stanley Nahman, Kristina Kintziger |
Spinal epidural abscess (SEA) is a serious complication in hemodialysis (HD) patients, yet there is little medical literature that discusses it. This analysis identifies risk factors and co-morbidities associated with SEA, as well as risk factors for mortality following the diagnosis using SAS. All incident HD cases from the United States Renal Data System for calendar years 2005 to 2008 were queried for a diagnosis of SEA. Potential clinical covariates, survival and risk factors were recovered using ICD-9 diagnosis codes. Log-binomial regressions were performed using PROC GENMOD to assess the relative risks, and Cox regression models were run using PROC PHREG to estimate hazard ratios for mortality |
hazard ratio, relative risk, proc step, sample, proc genmod, proc phreg, regression analysis | source | 4 |
| 268 | Risk-Based Monitoring of Clinical Trials Using JMP Clinical | Richard C. Zink | Guidelines from the International Conference on Harmonisation suggest that clinical trial data should be actively monitored to ensure data quality. Traditional interpretation of this guidance has often led to 100% source data verification (SDV) of respective case report forms through onsite monitoring. Such monitoring activities can also identify deficiencies in site training and uncover fraudulent behavior. However, such extensive onsite review is time- consuming, expensive, and as is true for any manual effort, limited in scope and prone to error. In contrast, risk- based monitoring makes use of central computerized review of clinical trial data and site metrics to determine if sites should receive more extensive quality review through on-site monitoring visits. We will demonstrate a risk-based monitoring solution within JMP Clinical to assess clinical trial data quality. Further, we'll describe a suite of tools used for identifying potentially fraudulent data at clinical sites | clinical trial, data cleaning, clinical data, outlier, adverse events, box plot, dataset, threshold, hierarchical clustering, vital signs, volcano plot, sample | source | 14 |
| 269 | Sailing Over the ACROSS Hurdle in PROC REPORT | Cynthia L. Zender | To get the full benefit from PROC REPORT, the savvy programmer needs to master ACROSS usage and the COMPUTE block. Timing issues with PROC REPORT and absolute column references can unlock the power of |
proc report, proc step, character variable, proc transpose, numeric variable, proc tabulate, sas log | source zip |
16 |
| 270 | SAS 9.4 Web Application Performance: Monitoring, Tuning, Scaling, and Troubleshooting | Rob Sioss | SAS 9.4 introduces several new software products to better support SAS web applications. These products include SAS Web Server, SAS Web Application Server (with the availability of out-of-the-box clustering), and SAS Environment Manager. Even though these products have been tuned and tested for SAS 9.4 web applications, advanced users might want to know the tools and techniques that they can use to further monitor, manage, tune, and improve the performance of their environment. This paper discusses how customers can achieve that by exploring the following concepts, activities, techniques, and tools: using SAS Environment Manager to monitor run-time performance of middle-tier components using additional tools to monitor middle-tier components (Apache server-status, Java VisualVM, Java command- line tools, Java GC logging) identifying the potential bottlenecks and tuning suggestions identifying appropriate clustering strategy (single-server vs | sample, connection pool, histogram | source | 17 |
| 271 | SAS Admins Need a Dashboard, Too | Patricia S. Aanderud, Michelle Homes |
Why would a SAS administrator need a dashboard? With the evolution of SAS 9, the SAS administrator's role has dramatically changed. Creating a dashboard on a SAS environment allows the SAS administrator an overview on the environment health, ensure resources are used as predicted, and provide a way to explore. SAS Visual Analytics allows you to quickly explore, analyze, and visualize data - so why not bring the two concepts together. In this session, you will learn tips for designing dashboards, loading what may seem like impossible data, and build visualizations that guide users toward the next level of analysis. Using the dashboard, SAS administrators will learn ways to determine the system health; and how to take advantage of external tools, such as the Metacoda software, to find additional insights and explore problem areas | cpu time, sas administrator, proc step, sas enterprise guide, memory usage, data mining, sas management console, business intelligence, proc hpsplit, proc sql, sample | source | 20 |
| 272 | SAS and Hadoop. 3rd Annual State of the Union. | Paul Kent | Presentation | libname statement, proc step, proc sql, group by, proc means, high-performance sas, input statement | source | 47 |
| 273 | SAS and Java Application Integration for Dummies | Neetha Sindhu, Hari Hara Sudhan, Mingming Wang |
Traditionally, Java w eb applications interact w ith back-end databases by means of JDBC/ODBC connections to retrieve and update data. With the grow ing need for real -time charting and complex analysis types of data representation on these types of w eb applications, SAS computing pow er can be put to use by adding a SAS w eb service layer betw een the application and the database. This paper show s how a SAS w eb service layer can be used to render data to a JAVA application in a summarized form using SAS Stored Processes. This paper also demonstrates how inputs can be passed to a SAS Stored Process based on w hich computations/summarizations are made before output parameter and/or output data streams are returned to the Java application. SAS Stored Processes are deployed as SAS BI Web Services using SAS Management Console, w hich are available to the JAVA application as a URL | web service, sas enterprise guide, macro variable | source | 7 |
| 274 | SAS as a Code Manipulation Language: An Example of Writing a Music Exercise Book with Lilypond and SAS. | Peter Timusk | Presentation | txt, excel, proc step | source | 21 |
| 275 | SAS Data Mining for Predictor Identification: Developing Strategies for High School Dropout Prevention | Wendy B. Dickinson, Morgan C. Wang |
The high school dropout problem has been called a national crisis. Nearly one-third of all high school students leave the public school system before graduating (Swanson, 2004), and the problem is particularly severe among minority students (Greene & Winters, 2005; U.S. Department of Education, 2006). Educators, researchers, and policymakers continue to work to identify effective dropout prevention strategies. One effective approach is to identify high-risk students at early stage then provide corresponding interventions to keep them in school. One of the strength of Educational Data Mining is to reveal hidden patterns and predict future performance by analyzing accessible student data. These predictive algorithms generated by predictive modeling then can be constructed as an early warning system. You might see various early warning systems adopted by schools, districts, and states | data cleaning, stepwise selection, regression analysis, correlation, data mining, imputation, main effect | source | 9 |
| 276 | SAS Enterprise Guide 5.1: A Powerful Environment for Programmers, Too! | 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 5.1 as their primary interface to SAS, while maintaining the flexibility of writing their own customized code. We explore: navigating and customizing the SAS Enterprise Guide environment 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 SAS Code Analyzer, Report Builder, and Document Builder adding Project 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. Our audience is SAS users who understand the basics of SAS programming and want to learn how to use SAS Enterprise Guide | sas enterprise guide, bar chart, sas programmer, excel, proc step, macro variable, dataset, sas log, sashelp, work library, missing value, temporary dataset | source | 32 |
| 277 | SAS Enterprise Guide--Your Gateway to SAS | Dave Shea | SAS Enterprise Guidehas become the place through which many SAS users access the power of the SAS System. Some like it, some loathe it, some have never known anything else. Enterprise Guide offers a point and click environment to constructing your code, but also allows the semi-colonista to have their place in the sun too | sas enterprise guide, proc step, proc tabulate, macro variable, dataset, automatic macro variable, excel, bar chart, proc sql, proc gchart, where statement, proc print | source | 14 |
| 278 | SAS for Bayesian Mediation Analysis | Miočević Milica, David P. MacKinnon |
Recent statistical mediation analysis research focuses on using Bayesian methods for several reasons including the ability to incorporate prior information about model parameters, ease of constructing intervals for simple and complex models, and exact inference for small samples without restrictive assumptions of conventional mediation analysis (Yuan & MacKinnon, 2009).The focus of this paper is how the Bayesian framework offers a solution to mediation analysis with small samples; namely, using diffuse prior distributions when there is no prior literature on the phenomenon being studied, and incorporating prior information into the analysis when there is existing knowledge about the expected magnitude of mediation effects. The SAS MCMC procedure allows researchers to use one of two simple and effective methods to incorporate their prior knowledge into the statistical analysis and obtain the posterior probabilities for quantities of interest, such as the mediated effect |
prior probability, covariance, posterior probability, mediation analysis, regression analysis, proc step, degrees of freedom, response variable, normal distribution, sample, predictor variable, confidence interval | source | 11 |
| 279 | SAS Format Optimization: SAS_PUT or UNPUT (Who's On First?) | David Wiehle | Changes in default behavior in the last few SAS releases have allowed for faster processing of SAS formats, especially for SAS/ACCESS customers. But, as with any performance enhancement, your results might vary. This paper describes: the differences between two important SAS format optimizations. how to tell which optimization is in effect. * a simple method to get the behavior you want. The target audience for this paper includes SAS/ACCESS customers, particularly those who have also licensed the SAS Scoring Accelerator for Teradata, DB2, Netezza, Greenplum, or Aster |
proc sql, options statement, proc step, format statement, sas log, order by, macro variable, create table, select, user-defined format, proc format, put statement, if-then-else, in-database processing | source | 19 |
| 280 | SAS Grid - What They Didn't Tell You | Manuel Nitschinger, Phillip Manschek |
Speed, precision, reliability - these are just 3 of the many challenges that today's banking institutions need to face. Join Austria's ERSTE GROUP Bank (ERSTE) on their road from monolithic processing toward a highly flexible processing infrastructure using SAS Grid Technology. This paper focuses on the central topics and decisions that go beyond the standard material about the product that are presented initially to SAS Grid Technology prospects. Topics covered range from how to choose the correct hardware and critical architecture considerations to the necessary adaptions of existing code and logic - all of which have shown to be a common experience for all the members of the SAS Grid community. After making the initial plans and successfully managing the initial hurdles, seeing it all come together makes you realize the endless possibilities for improving your processing landscape | sas enterprise guide, sas grid | source | 9 |
| 281 | SAS Grid Manager I/O: Optimizing SAS Application Data Availability for the Grid | Gregg Rohaly, Harry Seifert |
As organizations deploy SAS applications to produce the analytical results that are critical for solid decision making, they are turning to distributed grid computing operated by SAS Grid Manager. SAS Grid Manager provides a flexible, centrally managed computing environment for processing large volumes of data for analytical applications. Exceptional storage performance is one of the most critical components of implementing SAS in a distributed grid environment. When the storage subsystem is not designed properly or implemented correctly, SAS applications do not perform well, thereby reducing a key advantage of moving to grid computing. Therefore, a well-architected SAS environment with a high-performance storage environment is integral to clients getting the most out of their investment | sas grid, cloud storage, grid computing | source | 14 |
| 282 | SAS Grid Manager, SAS Visual Analytics, and SAS High-Performance Analytics: Sharing Hardware and More | Ken Gahagan | There are exciting new capabilities available from SAS High-Performance Analytics and SAS Visual Analytics. Current customers seek a deployment strategy that enables gradual migration to the new technologies. Such a strategy would mitigate the need for "rip and replace" and would enable resource utilization to evolve along a continuum rather than partitioning resources, which would result in underused computing or storage hardware. New customers who deploy a combination of SAS Grid Manager, SAS High-Performance Analytics, and SAS Visual Analytics seek to reduce the cost of computing resources and reduce data duplication and data movement by deploying these solutions on the same pool of hardware. When sharing hardware, it is important to implement resource management in order to help guarantee that resources are available for critical applications and processes. This paper discusses various methods for managing hardware resources in a multi-application environment | sas grid, proc step, stress testing | source | 20 |
| 283 | SAS in the Enterprise.a Primer on SAS Architecture for IT | Gary Spakes | How does the SAS server architecture fit within your IT infrastructure? What functional aspects does the architecture support? This session helps attendees understand the logical server topology of the SAS technology stack: resource and process management in-memory architecture * in-database processing The session also discusses process flow from data acquisition through analytical information to visual insight. IT architects, data administrators, and IT managers from all industries should leave with an understanding of how SAS has evolved to better fit into the IT enterprise and to help IT's internal customers make better decisions | sas grid, data governance | source | 4 |
| 284 | SAS Installations: So you want to install SAS? | Rafi Sheikh | The paper demonstrates the importance of preparation for a SAS installation using the SAS Office Analytics bundle as an example. There are many nuances, as well as requirements, that need to be addressed before an installation can occur. The majority of these requirements and preparations are similar yet differ slightly, according to the target operating system, whether it be Windows or *NIX (Unix and Linux) flavors. Our discussion will focus on these three topics: 1. Pre-installation considerations such as sizing, storage, and proper credentials 2. Third-party requirements 3. Install steps and requirements and post-install configuration In addition to discussing preparations for an installation, this paper also addresses potential issues and pitfalls to watch out for, as well as best practices | data cleaning, sas management console | source | 9 |
| 285 | SAS Macros 101 | Alex Chaplin | Presentation | macro facility, macro definition | source | 5 |
| 286 | SAS Predictive Asset Maintenance: Find Out Why Before It's Too Late! | George S. Habek | Are you wondering what is causing valuable machine assets to fail? What could the drivers be, and what is the likelihood of those failures? Do you want to be proactive rather than reactive? These are all areas in which SAS Predictive Asset Maintenance can help immensely. This solution provides an analytical framework to reduce the amount of unscheduled downtime and optimize maintenance cycles and costs. Key aspects of this paper include the following: Key business drivers for and capabilities of SAS Predictive Asset Maintenance Detailed analysis of the solution: o Data model o Explorations o Data Selections o Path I: Analysis Workbench---maintenance analysis and stability monitoring o Path II: Analysis Workbench---JMP, SAS Enterprise Guide, and SAS Enterprise Miner o Analytical case development using SAS Enterprise Miner, SAS Model Manager, and SAS Data Integration Studio o Portlet for reports within SAS Predictive Asset Maintenance | data mining, sas enterprise guide, sas model manager, likelihood function, association rules, calendar year, score code, histogram, proc freq, regression analysis, data cleaning | source | 19 |
| 287 | SAS Solutions to Identifying Hospital Readmissions | Weifeng Fan, Maryam Sarfarazi |
Hospital readmission rate has become a key indicator for measuring the quality of health care and is currently adopted by major healthcare stakeholders including the Centers for Medicare & Medicaid Services (CMS), the Agency for Healthcare Research and Quality (AHRQ), and the National Committee for Quality Assurance (NCQA). In the calculation of the readmission rate, it is often a challenging task to identify an eligible hospital readmission from the convoluted administrative claims data. By taking advantage of the flexibility and power of SAS programming tools, this paper proposes three different solutions - in both DATA STEP and PROC SQL - to help identify hospital readmissions more efficiently and accurately |
proc sql, proc step, insurance claims, where statement, lag function, proc transpose | source | 8 |
| 288 | SAS UNIX Utilities and What They Can Do for You | Jerry C. Pendergrass | The UNIX host group delivers many utilities that go unnoticed. What are these utilities, and what do they tell you about your SAS System? Are you having authentication problems? Are you unable to obtain a result from a workspace server? What hot fixes have you applied? These are subjects that come up during a SAS Technical Support call. It would be good to have background information about these tools before you have to use them | sample, sas base | source | 12 |
| 289 | SAS Visual Analytics Deliverers Insights into the UK University League Tables | Richard Self, Stuart Berry, Claire Foyle, Dave Voorhis |
Universities in the UK are now subject to League Table reporting by a range of providers. The criteria used by each League Table differ. Universities, their Faculties and individual Subject areas want to understand how the different tables are constructed and calculated and what is required in order to maximise their position in each league table in order to attract the best students to their institution, in order to maximise recruitment and student related income streams. The School of Computing and Maths at the University of Derby is developing the use SAS Visual Analytics to analyse each league table to provide actionable insights as to actions that can be taken to improve their relative standing in the league tables and also to gain insights into feasible levels of targets relative to the peer groups of institutions | sas visual analytics, outlier | source | 4 |
| 290 | SAS Visual Analytics for the Three Cs: Cloud, Consumerization, and Collaboration | Christopher Redpath, Nicholas Eayrs |
SAS Visual Analytics delivers the power of approachable in-memory analytics in an intuitive web interface. The scalable technology behind SAS Visual Analytics should not just benefit the analyst or data scientist in your organization, but, indeed, everyone regardless of their analytical backgrounds. This paper outlines a framework for the creation of a cloud deployment of SAS Visual Analytics using the SAS 9.4 platform. Based on proven best practices and existing customer implementations, the paper focuses on architecture, processes, and design for reliability and scalable multi-tenancy. The framework allows your organization to move away from the departmental view of the world and offer analytical capabilities for consumerization and collaboration across the enterprise | access control, sas base | source | 19 |
| 291 | SAS XML Programming Techniques | Christopher W. Schacherer | Due to XML's growing role in data interchange, it is increasingly important for SAS programmers to become proficient with SAS technologies and techniques for creating and consuming XML. The current work expands on a SAS Global Forum 2013 presentation that dealt with these topics-providing additional examples of using XML maps to read and write XML files and using the Output Delivery System (ODS) to create custom TAGSETS for generating XML |
libname engine, dataset, proc step, libname statement, proc template, missing value, proc print, insurance claims, macro variable, sas programmer, variable, ods html, sas library, web service, excel, proc sql | source | 39 |
| 292 | SAS/STAT 13.1 Round-Up | Bob Rodriguez, Maura E. Stokes |
SAS/STAT 13.1 brings valuable new techniques to all sectors of the audience for SAS statistical software. Updates for survival analysis include nonparametric methods for interval censoring and models for competing risks. Multiple imputation methods are extended with the addition of sensitivity analysis. Bayesian discrete choice models offer a modern approach for consumer research. Path diagrams are a welcome addition to structural equation modeling, and item response models are available for educational assessment. This paper provides overviews and introductory examples for each of the new focus areas in SAS/STAT 13.1. The paper also provides a sneak preview of the follow-up release, SAS/STAT 13.2, which brings additional strategies for missing data analysis and other important updates to statistical customers |
bootstrap, regression analysis, likelihood function, survival analysis, confidence interval, proc step, imputation, multiple imputation, estimate, bayesian statistics, proc mixed, proc hpgenselect, maximum likelihood estimation, proc nlin, proc phreg, proc genmod, proc glimmix, proc princomp | source | 25 |
| 293 | Scatter Plot Smoothing Using PROC LOESS and Restricted Cubic Splines | Jonas V. Bilenas | SAS has a number of procedures for smoothing scatter plots. In this tutorial, we review the nonparametric technique called LOESS, which estimates local regression surfaces. We review the LOESS procedure and then compare it to a parametric regression methodology that employs restricted cubic splines to fit nonlinear patterns in the data. Not only do these two methods fit scatterplot data, but they can also be used to fit multivariate relationships |
proc step, proc loess, regression analysis, restricted cubic spline, predictor variable, proc sgplot, response variable, loess, scatter plot, proc reg | source | 11 |
| 294 | Scatterplots: Basics, Enhancements, Problems, and Solutions | Peter L. Flom | The scatter plot is a basic tool for presenting information on two continuous variables. While the basic plot is good in many situations, enhancements can increase its utility. I also go over tools to deal with the problem of overplotting | scatter plot, regression analysis, box plot | source | 13 |
| 295 | Scenarios Where Utilizing a Spline Model in Developing a Regression Model Is Appropriate | Ning Huang | Linear regression has been a widely used | regression analysis, proc reg, anova, confidence interval, predictor variable, estimate | source | 9 |
| 296 | Secret Experts Exposed: Using Text Analytics to Identify and Surface Subject Matter Experts in the Enterprise | Richard Crowell, Saratendu Sethi, Xu Yang, Chunqi Zuo, Fruzsina Veress |
All successful organizations seek ways of communicating the identity of subject matter experts to employees. This information exists as common knowledge when an organization is first starting out, but the common knowledge becomes fragmented as the organization grows. SAS Text Analytics can be used on an organization's internal unstructured data to reunite these knowledge fragments. This paper demonstrates how to extract and surface this valuable information from within an organization. First, the organization's unstructured textual data are analyzed by SAS Enterprise Content Categorization to develop a topic taxonomy that associates subject matter with subject matter experts in the organization. Then, SAS Text Analytics can be used successfully to build powerful semantic models that enhance an organization's unstructured data. This paper shows how to use those models to process and deliver real-time information to employees, increasing the value of internal company information | excel, sample | source | 10 |
| 297 | Secrets from a SAS Technical Support Guy: Combining the Power of the Output Deliver System with Microsoft Excel Worksheets | Chevell T. Parker | Business analysts commonly use Microsoft EXCEL with the SAS System to answer difficult business questions. While you can use these applications independently of each other to obtain the information you need, you can also combine the power of those applications, using the SAS Output Delivery System (ODS) TAGSETS, to completely automate the process. This combination delivers a more efficient process that enables you to create fully functional and highly customized EXCEL worksheets within SAS. This paper starts by discussing common questions and problems that SAS Technical Support receives from users when they try to generate EXCEL worksheets. The discussion continues with methods for automating EXCEL worksheets using ODS TAGSETS and customizing your worksheets using the CSS style engine and extended TAGSETS |
excel, sample, proc step, proc template, output delivery system, proc sql, ods tagsets.excelxp, sas output, ods excel, macro definition, macro quoting function | source | 22 |
| 298 | Secure SAS OLAP Cubes with Top-Secret Permissions | Stephen M. Overton | SAS OLAP technology is used to organize and present summarized data for business intelligence applications. It features flexible options for creating and storing aggregations to improve performance and brings a powerful multi- dimensional approach to querying data. This paper focuses on managing security features available to OLAP cubes through the combination of SAS metadata and MDX logic | raw data, sas management console | source | 11 |
| 299 | Secure Your Analytical Insights on the Plane, in the Café, and on the Train with SAS Mobile BI | Christopher Redpath, Meera Venkataramani |
Security-conscious organizations have rigorous IT regulations, especially when company data is available on the move. This paper explores the options available to secure a deployment of SAS Mobile BI with SAS Visual Analytics. The setup ensures encrypted communication from remote mobile clients all the way to back-end servers. In addition, the integration of SAS Mobile BI with third-party Mobile Device Management software and Virtual Private Network technology allow you to place several layers of security and access control to your data. The paper also covers the out-of-the-box security features of the SAS Mobile BI and SAS Visual Analytics administration applications to help you close the loop on all possible areas of exploitation | access control, sas visual analytics | source | 8 |
| 300 | Security Scenario for SAS Visual Analytics | Dawn Schrader | Even if you are familiar with security considerations for SAS BI deployments, such as metadata and file system permissions, there are additional security aspects to consider when securing any environment that includes SAS Visual Analytics. These include files and permissions to the grid machines in a distributed environment, permissions on the SAS LASR Analytic Servers, and interactions with existing metadata types. We approach these security aspects from the perspective of an administrator who is securing the environment for himself, a data builder, and a report consumer | batch job, libname statement | source | 9 |
| 301 | Selecting Peer Institutions with Cluster Analysis | Diana D. Suhr | Universities strive to be competitive in the quality of education as well as cost of attendance. Peer institutions are selected to make comparisons pertaining to academics, costs, and revenues. These comparisons lead to strategic decisions and long-range planning to meet goals. The process of finding comparable institutions could be completed with cluster analysis, a statistical technique. Cluster analysis places universities with similar characteristics into groups or clusters. A process to determine peer universities will be illustrated using PROC STANDARD, PROC FASTCLUS, and PROC CLUSTER |
cluster analysis, proc step, proc cluster, proc fastclus, hierarchical clustering, distance measure, proc standard, standard deviation, scatter plot, proc freq, discriminant analysis, factor analysis, dataset, agglomerative clustering, euclidean distance, sample, proc princomp, outlier, proc tree | source | 12 |
| 302 | Sensitivity Analysis in Multiple Imputation for Missing Data | Yang Yuan | Multiple imputation, a popular strategy for dealing with missing values, usually assumes that the data are missing at random (MAR). That is, for a variable Y, the probability that an observation is missing depends only on the observed values of other variables, not on the unobserved values of Y. It is important to examine the sensitivity of inferences to departures from the MAR assumption, because this assumption cannot be verified using the data. The pattern-mixture model approach to sensitivity analysis models the distribution of a response as the mixture of a distribution of the observed responses and a distribution of the missing responses. Missing values can then be imputed under a plausible scenario for which the missing data are missing not at random (MNAR). If this scenario leads to a conclusion different from that of inference under MAR, then the MAR assumption is questionable | imputation, sensitivity analysis, regression analysis, clinical trial, estimate, treatment effect, proc calis | source | 12 |
| 303 | Seven Steps to a SAS Enterprise BI Proof-of-Concept | Sheryl Weise | The Purchasing Department is considering contracting with your team for a new SAS Enterprise Business Intelligence application. He's already met with SAS and seen the sales pitch, and he is very interested. But the manager is a tightwad and not sure about spending the money. Also, he wants his team to be the primary developers for this new application. Before investing his money on training, programming, and support, he would like a proof-of- concept. This paper will walk you through the 7 steps to create a SAS Enterprise BI POC project: 1. Develop a kick-off meeting including a full demo of the SAS Enterprise BI tools. 2. Set up your Linux filesystems and security. 3. Set up your SAS metadata ACTs, users, groups, folders, and libraries. 4. Make sure the necessary SAS client tools are installed on the developers' machines. 5 | sas enterprise guide, sas management console, business intelligence, access control, sas programmer, sample | source | 13 |
| 304 | Share Your SAS Visual Analytics Reports with SAS Office Analytics | David Bailey, I-Kong B. Fu, Anand V. Chitale |
SAS Visual Analytics allows you to conduct ad hoc data analysis, visually explore data, develop reports, and then share insights through web and mobile tablet apps. You can now also share your insights with colleagues using the SAS Office Analytics integration with Microsoft EXCEL, Microsoft Word, Microsoft PowerPoint, Microsoft Outlook, and Microsoft SharePoint. In addition to opening and refreshing reports created using SAS Visual Analytics, a new SAS Central view allows you to manage and comment on your favorite and recent reports from your Microsoft Office applications. You can also view your SAS Visual Analytics results in SAS Enterprise Guide. Learn more about this integration and what is coming in the future in this breakout session |
excel, sas enterprise guide, sas visual analytics | source | 17 |
| 305 | Simple ODS Tips to Get RWI (Really Wonderful Information) | Gina Huff | SAS continues to expand and improve its reporting capability. With new 9.4 enhancements in ODS (Output Delivery System), the opportunity to create stunning reports has expanded even further. If you are charged with creating relevant, informative, easy-to-read reports for clients and/or administrators, then the ODS Report Writing Interface, ODS LAYOUT enhancements, and the new ODS TEXT procedure are important tools to use. These tools allow you to create reports in a smart, eye-catching format that can be turned around quite quickly and programmed to provide optimum flexibility |
proc step, output delivery system, bar chart, macro variable, proc sgplot, data step, if-then-else, ods pdf, macro definition | source | 14 |
| 306 | Simulating Portfolio Losses from Adverse Events: Applications to the Insurance and Finance Industries | Mahesh V. Joshi, Jan Chvosta |
Companies in the insurance and banking industries need to model the frequency and severity of adverse events every day. Accurate modeling of risks and the application of predictive methods ensure the liquidity and financial health of portfolios. Often, the modeling involves computationally intensive, large-scale simulation. SAS/ETS provides high-performance procedures to assist in this modeling. This paper discusses the capabilities of the HPCOUNTREG and HPSEVERITY procedures, which estimate count and loss distribution models in a massively parallel processing environment. The loss modeling features have been extended by the new HPCDM procedure, which simulates the probability distribution of the aggregate loss by compounding the count and severity distribution models. PROC HPCDM also analyzes the impact of various future scenarios and parameter uncertainty on the distribution of the aggregate loss |
proc step, sample, regression analysis, claim cost model, proc freq, loss event, probability distribution, standard deviation, insurer, estimate, poisson distribution, banking, scenario analysis, binomial distribution | source zip |
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| 307 | Simulation of MapReduce with the Hash-of-Hashes Technique | Joseph W. Hinson | Big data is all the rage these days, with the proliferation of data-accumulating electronic gadgets and instrumentation. At the heart of big data analytics is the MapReduce programming model. As a framework for distributed computing, MapReduce uses a divide-and-conquer approach to allow large-scale parallel processing of massive data. As the name suggests, the model consists of a Map function, which first splits data into key-value pairs, and a Reduce function, which then carries out the final processing of the mapper outputs. It is not hard to see how these functions can be simulated with the SAS hash objects technique, and in reality, implemented in the new SAS DS2 language. This paper demonstrates how hash object programming can handle data in a MapReduce fashion and shows some potential applications in physics, chemistry, biology, and finance | hash of hashes | source | 18 |
| 308 | So Much Software, So Little Time: Deploying SAS Onto Oodles of Machines | Mark L. Schneider | Distributing SAS software to a large number of machines can be challenging at best and exhausting at worst. Common areas of concern for installers are silent automation, network traffic, ease of setup, standardized configurations, maintainability, and simply the sheer amount of time it takes to make the software available to end users. We describe a variety of techniques to ease the pain of provisioning SAS software, including the new stand- alone SAS Enterprise Guide and SAS Add-In for Microsoft Office installers, as well as the tried and true SAS Deployment Wizard record and playback functionality. We also cover ways to shrink SAS Software Depots, like the new "subsetting recipe" feature, in order to ease scenarios requiring depot redistribution. Finally, we touch on alternate methods for workstation access to SAS client software, including application streaming, desktop virtualization, and Java Web Start | sas enterprise guide, data mining | source | 13 |
| 309 | Something for Nothing! Converting Plots from SAS/GRAPH to ODS Graphics | Philip R. Holland | All the documentation about the creation of graphs with SAS software states that ODS Graphics is not intended to replace SAS/GRAPH. However, ODS Graphics is included in the Base SAS license from SAS 9.3, but SAS/GRAPH still requires an additional component license, so there is definitely a financial incentive to convert to ODS Graphics. This paper gives examples that can be used to replace commonly created SAS/GRAPH plots, and highlights the small number of plots that are still very difficult, or impossible, to create in ODS Graphics |
bar chart, proc step, horizontal bar chart, vertical bar chart, proc template, confidence interval, proc gchart, sample, regression analysis | source | 17 |
| 310 | Speed Dating: Looping Through a Table Using Dates | Scott Fawver | Have you ever needed to use dates as values to loop through a table? For example, how many events occurred by 1, 2 , 3 … n months ahead. Maybe you just changed the dat es manually and re-ran the query n times? This is a common need in economic and behavioral sciences. This presentation demonstrates how to create a table of dates that can be used with SAS macro variables to loop through a table. Using this dates table in combination with the SAS do loop ensures accuracy and saves time | macro variable, time series analysis | source | 5 |
| 311 | Star Wars and the Art of Data Science: An Analytical Approach to Understanding Large Amounts of Unstructured Data | Adam Maness, Mary Osborne |
Businesses today are inundated with unstructured data-not just social media, but books, blogs, articles, journals, manuscripts, and even detailed legal documents. Manually managing unstructured data can be time consuming and frustrating, and might not yield accurate results. Having an analyst read documents often introduces bias because analysts have their own experiences, and often those experiences help shape how the text is interpreted. The fact that people become fatigued can also impact the way the text is interpreted. Is the analyst as motivated at the end of the day as they are at the beginning? Data science involves using data management, analytical, and visualization strategies to uncover the story the data is trying to tell in a more automated fashion. This is important with structured data, but becomes even more vital with unstructured data. Introducing automated processes for managing unstructured data can significantly increase the value and meaning gleaned from the data | unstructured data, data science, data wrangling, proc freq, raw data, character variable, dataset | source | 28 |
| 312 | Stylish Waterfall Graphs Using SAS 9.3 and SAS 9.4 Graph Template Language | Setsuko Chiba | One stylish graph provides a clear picture of data summaries reported on tables and listings. Waterfall graphs show at a glance the increase/decrease of data analysis results from various industries. The introduction of SAS 9.2 Statistical Graphics (SG) procedures enabled SAS programmers of all skill levels to produce high quality results with simple coding. Moreover, SAS programmers can create sophisticated graphs in stylish custom layouts using SAS 9.3 and later Graph Template Language (GTL) with Output Delivery System (ODS) Style Template. This poster presents two sets of example waterfall graphs in clinical trials using SAS 9.3 and later. Example 1 for presentation: displays colorful graphs using SAS 9.3 and 9.4 new options. Example 2 for publication: displays simple graphs with Gray Scale Color Coding and patterns. You can create these graphs on Unix or Windows |
waterfall chart, graph template language, proc step, sample, proc template | source | 6 |
| 313 | Summarizing and Highlighting Differences in Senate Race Data Using SAS Sentiment Analysis | Hilke Reckman, Michael Wallis, Richard Crowell, Linnea Micciulla, Cheyanne Baird |
Contrasting two sets of textual data points out important differences. For example, consider social media data that have been collected on the race between incumbent Kay Hagan and challenger Thom Tillis in the 2014 election for the seat of US Senator from North Carolina. People talk about the candidates in different terms for different topics, and you can extract the words and phrases that are used more in messages about one candidate than about the other. By using SAS Sentiment Analysis on the extracted information, you can discern not only the most important topics and sentiments for each candidate, but also the most prominent and distinguishing terms that are used in the discussion. Find out if Republicans and Democrats speak different languages! | source | 10 | |
| 314 | Summarizing Data for a Systematic Review | Ravi Gaddameedi, Usha Kreaden |
standard deviation, weighted mean, inferential statistics, temporary dataset, proc step | source | 6 | |
| 315 | Supporting SAS Software 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 characterization of survey 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 in frastructure to support its deployment and use. This paper provides an overview of Westat's SAS support infrastructure, which supplies resources that are aimed at educating staff, strengthening their SAS skills, providing SAS technical support, and keeping the staff on the cutting edge of SAS programming techniques | sas programming, macro definition | source | 9 |
| 316 | Survival of Your Heart: Analyzing the Effect of Stress on a Cardiac Event and Predicting the Survival Chances | Yogananda Domlur Seetharama, Sai Vijay Kishore Movva |
Presentation | source | 3 | |
| 317 | Tables, Listings, and Figures: Replaying Rather than Appending | William Coar | In day-to-day operations of a Biostatistics and Statistical Programming department, we are often tasked with generating reports in the form of tables, listings, and figures (TLFs). Some requests come in the form of a small number of TLFs whereas others are more substantial in magnitude. Regardless, creating a single document for distribution and review may be required after all TLFs have been completed. A common setting in the pharmaceutical industry is to develop SAS code in which individual programs generate one or more TLFs in some standard formatted output such as RTF or PDF with a common look and feel. Furthermore, programs are developed over time, with the production run being in batch mode. The result is a set of TLFs completed at different times. Creation of a final (single) document with a properly sectioned and hyperlinked Table of Contents as well as dynamic page numbering may be desired | proc step, proc report, delete, order by | source | 7 |
| 318 | Techniques in Processing Data on Hadoop | Donna De Capite | Before you can analyze your big data, you need to prepare the data for analysis. This paper discusses capabilities and techniques for using the power of SAS to prepare big data for analytics. It focuses on how a SAS user can write code that will run in a Hadoop cluster and take advantage of the massive parallel processing power of Hadoop | proc step, data mining, big data, proc sql, macro variable | source | 11 |
| 319 | Test for Success: Automated Testing of SAS Metadata Security Implementations | Paul Homes | SAS platform installations are large, complex, growing, and ever-changing enterprise systems that support many diverse groups of users and content. A reliable metadata security implementation is critical for providing access to business resources in a methodical, organized, partitioned, and protected manner. With natural changes to users, groups, and folders from an organization's day-to-day activities, deviations from an original metadata security plan are very likely and can put protected resources at risk. Regular security testing can ensure compliance, but, given existing administrator commitments and the time consuming nature of manual testing procedures, it doesn't tend to happen. This paper discusses concepts and outlines several example test specifications from an automated metadata security testing framework being developed by Metacoda | access control, sas administrator, sample | source | 13 |
| 320 | Text Analytics: Predicting the Success of Newly Released Free Android Apps Using SAS Enterprise Miner and SAS Sentiment Analysis Studio | Vandana Reddy, Chinmay Dugar |
Presentation | proc reg, regression analysis, data mining | source | 3 |
| 321 | Text Mining Economic Topic Sentiment for Time Series Modeling | Michael P. Dessauer, Justin Kauhl |
Global businesses must react to daily changes in market conditions over multiple geographies and industries. Consuming reputable daily economic reports assists in understanding these changing conditions, but requires both a significant human time commitment and a subjective assessment of each topic area of interest. To combat these constraints, Dow's Advanced Analytics team has constructed a process to calculate sentence-level topic frequency and sentiment scoring from unstructured economic reports. Daily topic sentiment scores are aggregated to weekly and monthly intervals and used as exogenous variables to model external economic time series data. These models serve to both validate the relationship between our sentiment scoring process and also as near-term forecasts where daily or weekly variables are unavailable. This paper will first describe our process of using SAS Text Miner to import and discover economic topics and sentiment from unstructured economic reports | time series analysis, feature selection, proc step, proc freq, sample, series plot, unstructured data, data wrangling, correlation, bubble plot, raw data, parsing, data cleaning, regression analysis, data mining | source | 14 |
| 322 | Text Mining Reveals the Secret of Success: Identification of Sales Determinants Hidden in Customers' Opinions | Rafal Wojdan | Nowadays, in the Big Data era, Business Intelligence Departments collect, store, process, calculate, and monitor massive amounts of data. Nevertheless, sometimes hundreds of metrics built on the structured data are inefficient to explain why the offered deal sold better or worse than expected. The answer might be found in text data that every company owns and yet is not aware of its possible usage or neglects its value. This project shows text mining methods, implemented in SAS Text Miner 12.1, that enable the determination of a deal's success or failure factors based on in-house or Internet-scattered customers' views and opinions. The study is conducted on data gathered from Groupon Sp. z o.o. (Polish business unit) - e-commerce company, as it is assumed that the market is by and large a customer-driven environment | proc step, proc hpsplit, proc freq, regression analysis, proc groovy, sample, business intelligence, neural network, macro variable, network optimization, sample size, proc import, proc http | source | 18 |
| 323 | The Armchair Quarterback: Writing SAS Code for the Perfect Pivot (Table, That Is) | Peter W. Eberhardt | "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 |
| 324 | The Commonly Used Statistical Methods to Control The Probability of an Overall Type I Error and the Application in Clinical Trial | Lixiang Yao | In a clinical study, we often set up multiple hypotheses with regard to the cost of getting study result. However, the multiplicity problem arises immediately when they are performed in a univariate manner. Some methods to control the rate of the overall type I error are applied widely, and they are discussed in this paper. Except the methodology, we will introduce its application in one study case and provide the SAS code using PROC MULTITEST |
type i error, significance level, p-value, null hypothesis, clinical trial | source | 7 |
| 325 | The Desert and the Dunes: Finding Oases and Avoiding Mirages with the SAS Visual Analytics Explorer | Nascif Abousalh-Neto | Once upon a time, a writer compared a desert to a labyrinth. A desert has no walls or stairways, but you can still find yourself utterly lost in it. And oftentimes, when you think you found that oasis you were looking for, what you are really seeing is an illusion, a mirage. Similarly, logical fallacies and misleading data patterns can easily deceive the unaware data explorer. In this paper, we discuss how they can be recognized and neutralized with the power of the SAS Visual Analytics Explorer. Armed with this knowledge, you will be able to safely navigate the dunes to find true insights and avoid false | correlation, regression analysis, data science, exploratory data analysis, data wrangling, proc reg, outlier, time series analysis, sample, proc freq, sas visual analytics, time series data, proc means | source | 15 |
| 326 | The Latest Tuning Guidelines for Your Hardware Infrastructure | Margaret A. Crevar, Tony Brown |
We continually work with our hardware partners to establish best practices with regard to tuning the latest hardware components that are released each year. This paper goes over the latest tuning guidelines for your hardware infrastructure, including your host computer system, operating system, and complete I/O infrastructure (from the computer host and network adapters down through the physical storage). Our findings are published in SAS papers on the SAS website, support.sas.com, with updates posted to the SAS Administration blog | sas grid, grid computing | source | 3 |
| 327 | The Many Ways of Creating Dashboards Using SAS | Mark Bodt | For decades, SAS has been the cornerstone of many organizations for business reporting. In more recent times, the ability to quickly determine the performance of an organization through the use of dashboards has become a requirement. Different ways of providing dashboard capabilities are discussed in this paper: using out of the box solutions such as SAS Visual Analytics, SAS BI Dashboard, through to alternative solutions using SAS Stored Processes, batch processes and SAS Integration Technologies. Extending the available indicators is also discussed using Graph Template Language, KPI indicators provided with Base SAS, as well as alternatives such as Google Charts and Flash objects. Real-world field experience, problem areas, solutions and tips are shared along with live examples of some of the different methods | graph template language, proc step, sample, sas base, sas output, macro variable | source | 16 |
| 328 | The Power of PROC FORMAT | Jonas V. Bilenas | The FORMAT procedure in SAS is a very powerful and productive tool, yet many beginning programmers rarely make use of it. The FORMAT procedure provides a convenient way to do a table lookup in SAS. User-generated FORMATS can be used to assign descriptive labels to data values, create new variables, and find unexpected values. PROC FORMAT can also be used to generate data extracts and to merge data sets. This paper provides an introductory look a t PROC FORMAT for the beginning user and provides sample code that illustrates the power of PROC FORMAT in a number of applications. Additional examples and applications of PROC FORMAT can be found in the SAS Press book titled "The Power of PROC FORMAT." |
proc format, proc step, lookup, dataset, proc freq, missing value, proc tabulate, char., put statement, format statement | source | 8 |
| 329 | The Query Builder: The Swiss Army Knife of SAS Enterprise Guide | Jennifer First-Kluge, Steven J. First |
The SAS Enterprise Guide Query Builder is one of the most powerful components of the software. It enables a user to bring in data, join, drop and add columns, compute new columns, sort, filter data, leverage the advanced expression builder, change column attributes, and more! This presentation provides an overview of the major features of this powerful tool and how to leverage it every day | sas enterprise guide, proc sql, dataset, proc step | source | 7 |
| 330 | The RAKE-TRIM Algorithm: Reducing Variance and Bias in Sampling Weights | Louis Rizzo | Raking (iterative proportional fitting) is a procedure that takes sampling weights from complex sample surveys and adjusts them so that they add to known control totals. This process reduces variance and adjusts for undercoverage. But raking in multiple dimensions can lead to extreme weights, which increase variance. Trimming is another sample weighting procedure that reduces extreme weights to cutoffs, thereby improving variance properties while potentially introducing bias. The RAKE-TRIM macro combines raking and trimming in an iterative algorithm to achieve these two goals simultaneously. The raking reduces the bias potential from trimming, and the trimming reduces the variance inflation from raking. When convergence occurs, the final weights aggregate to the control totals, as well as respect the trimming limits. SAS macros are well suited for this kind of envelope program: the larger macro consists of the integration of component macros that were developed for other applications | control total, sample, excel, survey weight, macro definition, raking, sample weight | source | 8 |
| 331 | The Role of Customer Response Models in Customer Solicitation Center's Direct Marketing Campaign | Arun Mandapaka, Amit Kushwah, Goutam Chakraborty |
Direct Marketing is the practice of delivering promotional messages directly to customers or prospects on an individual basis rather than using a mass medium. In this project, we build a finely tuned response model that helps a financial services company to select high quality receptive customers for their future campaigns and identify the important factors that influence the marketing campaign to effectively manage their resources. This study was based on the customer solicitation center's marketing campaign data (45,211 observations and 18 variables) available on UC Irvine website with attributes of present & past campaign information (communication type, contact duration, previous campaign outcome etc.) and customer's personal & banking information. As part of data preparation, we had performed decision tree imputation to handle missing values and categorical recoding for reducing levels of class variables | logistic regression, missing value, regression analysis, logistic distribution, proc hpsplit, data mining, support vector machine, data cleaning, nominal variable, neural network, financial services, response variable, sample | source | 12 |
| 332 | 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, hash iterator, out=, lookup, conditional do loop, order by, sysrc, data merging, proc step, sample | source | 17 |
| 333 | The Soccer Oracle: Predicting Soccer Game Outcomes Using SAS Enterprise Miner | Vandana Reddy, Sai Vijay Kishore Movva |
Presentation | proc hpsplit, gradient boosting, neural network, data mining, regression analysis, response variable | source | 3 |
| 334 | The Traveling Baseball Fan Problem and the OPTMODEL Procedure | Tonya Chapman, Matthew V. Galati, Robert C. Pratt |
In the traveling salesman problem, a salesman must minimize travel distance while visiting each of a given set of cities exactly once. This paper uses the SAS/OR OPTMODEL procedure to formulate and solve the traveling baseball fan problem, which complicates the traveling salesman problem by incorporating scheduling constraints: a baseball fan must visit each of the 30 Major League ballparks exactly once, and each visit must include watching a scheduled Major League game. The objective is to minimize the time between the start of the first attended game and the end of the last attended game. One natural integer programming formulation involves a binary decision variable for each scheduled game, indicating whether the fan attends. But a reformulation as a side-constrained network flow problem yields much better solver performance |
cpu time, odds ratio | source | 27 |
| 335 | The Use of Analytics for Insurance Claim Fraud Detection: A Unique Challenge | Roosevelt Mosley, Nick Kucera |
As it has been widely reported in the insurance trade news, fraudulent claims continue to be a significant issue in the insurance industry, costing policyholders billions of dollars. More companies are turning to analytics to help identifying claim fraud. Identifying claim fraud using predictive analytics, however, represents a unique challenge. 1. Most predictive analytics applications have a complete target variable which can be analyzed. Fraud is unique in that there is generally a lot of fraud that has occurred historically that has not been identified. Therefore, the definition of the target variable is not complete. 2. There is a natural assumption that the past will bear some resemblance to the future. In the case of fraud, methods of defrauding insurance companies change quickly and it can make the analysis of a historical database less valuable for identifying future fraud. 3 | likelihood function, claims fraud, insurer, outlier, predictive analytics, cluster analysis, proc hpsplit, rmse, type i error, proc princomp, mse, insurance, response variable, predictor variable | source | 15 |
| 336 | Three Different Ways to Import JSON from the Facebook Graph API | Philip James Busby | HTML5 has become the de facto standard for web applications. As a result, the lingua franca object notation of the web services that the web applications call has switched from XML to JSON. JSON is remarkably easy to parse in JavaScript, but so far SAS doesn't have any native JSON parsers. The Facebook Graph API dropped XML support a few years ago. This paper shows how we can parse the JSON in SAS by calling an external script, using PROC GROOVY to parse it inside of SAS, or by parsing the JSON manually with a DATA STEP. We'll extract the data from the Facebook Graph API and import it into an OLAP data mart to report and analyze a marketing campaign's effectiveness |
proc groovy, proc step | source | 5 |
| 337 | Time Contour Plots | David J. Corliss | This new SAS tool is two -dimensional color chart for visualizing changes in a population or system over time. Data for one point in time appear as a thin horizontal band of color. Bands for successive periods are stacked up to make a two-dimensional plot, with the vertical direction showing changes over time. As a system evolves over time, different kinds of events have different characteristic pa tterns. Creation of Time Contour Plots is explained step by step. Examples are given in astrostatistics, biostatistics, econometrics and demographics. Keywords: Data Visualization, PROC CONTOUR, Time Series Analysis |
contour plot, interval variable, time series analysis, proc step | source | 9 |
| 338 | Time Series Mapping with SAS: Visualizing Geographic Change over Time in the Health Insurance Industry | Barbara B. Okerson | Changes in health insurance and other industries often have a spatial component. Maps can be used to convey this type of information to the user more quickly than tabular reports and other non-graphical formats. SAS provides programmers and analysts with the tools to not only create professional and colorful maps, but also the ability to display spatial data on these maps in a meaningful manner that aids in the understanding of the changes that have transpired. This paper illustrates the creation of a number of different maps for displaying change over time with examples from the health insurance arena | health insurance, proc gmap, proc step, dataset, time series analysis | source | 8 |
| 339 | Tips and Tricks for Organizing and Administering Metadata | Michael G. Sadof | The SAS Management Console was designed to control and monitor virtually all of the parts and features of the SAS Intelligence Platform . However, administering even a small SAS B usiness Intelligence system can be a daunting task. This paper will present a few techniques that will help you simplify your administrative tasks and enable you and your user community to get the most out of your system. The SAS Metadata server stores most of the information required to maintain and run the SAS Intelligence Platform which is obviously the heart of SAS BI. It stores information about libraries, users, database logons , passwords, stored processes, reports, OLAP cubes and a myriad of other information. Organization of this metadata is an essential part of an optimally performing system. This paper will discuss ways of organizing the metadata to serve you organization well | spk file, sas management console | source | 6 |
| 340 | Tips and Tricks to Using SAS Enterprise Guide in a BI World | Angela M. Hall | No need to fret, Base SAS programmers. Converting to SAS Enterprise Guide is a breeze, and it provides so many advantages. Coding remote connections to SAS servers is a thing of the past. Generate WYSIWYG prompts to increase the usage of the SAS code and to create reports and SAS Stored Processes to share easily with people who don't use SAS Enterprise Guide. The first and most important thing, however, is to change the default options and preferences to tame SAS Enterprise Guide, making it behave similar to your Base SAS ways. I cover all of these topics and provide demos along the way | sas enterprise guide, sas base, dataset, sas programmer | source | 9 |
| 341 | Tips for Moving from Base SAS 9.3 to SAS Enterprise Guide 5.1 | Anjan Matlapudi | As a longtime Base SAS programmer, whether to use a different application for programming is a constant question when powerful applications such as SAS Enterprise Guide are available. This paper provides some important tips for the programmer, such as the best way to use the code window and how to take advantage of system-generated code in SAS Enterprise Guide 5.1. This paper also explains the differences between some of the functions and procedures in Base SAS and SAS Enterprise Guide. It highlights features in SAS Enterprise Guide such as process flow, data access management, and report automation, including formatting using XML tag sets | sas enterprise guide, sas base, proc step, sas log, excel, libname statement, proc report, proc sql, proc print, macro variable | source | 22 |
| 342 | Tips to Use Character String Functions in Record Lookup | Anjan Matlapudi | This paper gives you a better idea of how and where to use the record lookup functions to locate observations where a variable has some characteristic. Various related functions are illustrated to search numeric and character values in this process. Code is shown with time comparisons. I will discuss possible three ways to retrieve records using in SAS DATA STEP, PROC SQL and Perl regular expression. Real and CPU time processing issues will be highlighted when comparing to retrieve records using these |
proc sql, proc step, character variable, cpu time, where statement | source | 12 |
| 343 | Toe to Toe: Comparing ODS LAYOUT and the ODS Report Writing Interface | Daniel Kummer | Two new production features offered in the Output Delivery System (ODS) in SAS 9.4 are ODS LAYOUT and the ODS Report Writing Interface. This one-two punch gives you power and flexibility in structuring your SAS output. What are the strengths for each? How do they differ? How do they interact? This paper highlights the similarities and differences between the two and illustrates the advantages of using them together. Why go twelve rounds? Make your report a knockout with ODS LAYOUT and the Report Writing Interface |
output delivery system, bar chart, proc step, proc gchart, end, ods pdf, by group processing | source | 27 |
| 344 | Tools of the SAS Trade: A Centralized Macro-Based Reporting System | Patrick Thornton | This paper introduces basic-to-advanced strategies and syntax, the tools of the SAS trade, that enable client-quality PDF output to be delivered through a production system of macro programs. A variety of PROC REPORT output with proven client value serves to illustrate a discussion of the fundamental syntax used to create and share formats, macro programs, PROC REPORT output, inline styles, and style templates. The syntax is integrated into basic macro programs that demonstrate the core functionality of the reporting system. Later sections of the paper describe in detail the macro programs used to start and end a PDF: (a) programs to save all current titles, footnotes, and option settings, establish standard titles, footnotes and option settings, and initially create the PDF document; and (b) programs to create a final standard data documentation page, end the PDF, and restore all original titles, footnotes, and option settings |
proc report, ods pdf, proc step, macro variable, macro definition, macro loop, macro conditional, options statement, stored macro, proc freq | source | 20 |
| 345 | Top 10 Resources Every SAS Administrator Should Know About | Margaret A. Crevar, Tony Brown |
When assisting SAS customers who are experiencing performance issues, we are often asked by the SAS users at a customer site for the top 10 guidelines to share with those who have taken on the role of system administrator or SAS administrator. This paper points you to where you can get more information regarding each of the guidelines and related details on the SAS website | sas grid, sas 9, sas administrator | source | 5 |
| 346 | Top 10 SQL Tricks in SAS | Chao Huang | One great thing separating SAS from other statistical languages is that SAS has native SQL (Structured Query Language) capacity by its SQL procedure . In addition to its CURD (Create, Read, Update and Delete) features, SQL also significantly enhances SAS's power in descriptive statistics and data management. In this paper, we introduce ten useful tips by the SQL procedure for everyday practice of |
proc sql, proc step | source | 4 |
| 347 | Top Seven Techniques for Creating SAS Web Applications | Yogendra Joshi | Do you often create SAS web applications? Do you need to update or retrieve values from a SAS data set and display those values in a browser? Do you need to show the results of a SAS Stored Process in a browser? Are you finding it difficult to figure out how to pass parameters from a web page to a SAS Stored Process? If you answered yes to any of these questions, then look no further | regression analysis, correlation, response variable, predictor variable, put statement, proc step, proc corr, macro definition, proc reg | source | 20 |
| 348 | Tricks Using SAS Add-In for Microsoft Office | Patricia S. Aanderud | SAS Add-In for Microsoft Office remains a popular tool for people who are not SAS programmers due to its easy interface with the SAS servers. In this session, you'll learn some of the many tricks that other organizations use for getting more value out of the tool | excel, dataset, where statement, trouble ticket, sas enterprise guide | source | 18 |
| 349 | Trimmed_t: A SAS Macro for the Trimmed T-Test | Patricia Rodríguez de Gil, Anh P. Kellermann, Diep T. Nguyen, Eun Sook Kim, Jeffrey D. Kromrey |
The independent means t-test is commonly used for testing the equality of two population means. However, this test is very sensitive to violations of the population normal ity and homogeneity of variance assumptions. In such situations, Yuen's (1974) trimmed t-test is recommended as a robust alternative. The purpose of this paper is to provide a SAS macro that allows easy computation of Yuen's symmetric trimmed t-test. The macro output includes a table with trimmed means for each of two groups , Winsorized variance estimates, degrees of freedom, and obtained value of t (with two-tailed p-value). In addition, the results of a simulation study are presented and provide empirical comparisons of the Type I error rates and statistical power of the independent samples t-test, Satterthwaite's approximate t-test and the trimmed t-test when the assumptions of normality and homogeneity of variance are violated | type i error, sample, type i error control, sample size, significance level, trimming, degrees of freedom, f-test, power analysis, treatment effect, proc calis, macro definition | source | 13 |
| 350 | Turn Your SAS Macros into Microsoft Excel Functions with the SAS Integrated Object Model and ADO | Chris Brooks | As SAS professionals, we often wish our clients would make more use of the many excellent SAS tools at their disposal. However, it remains an indisputable fact that for many business users, Microsoft EXCEL is still their go-to application when it comes to carrying out any form of data analysis. There have been many attempts to integrate SAS and EXCEL, but none of these has up to now been entirely seamless. This paper addresses that problem by showing how, with a minimum of VBA (Visual Basic for Applications) code and by using the SAS Integrated Object Model (IOM) together with Microsoft's ActiveX Data Objects (ADO), we can create an EXCEL User Defined Function (UDF) that can accept parameters, carry out all data manipulations in SAS, and return the result to the spreadsheet in a way that is completely invisible to the user. They can nest or link these functions together just as if they were native EXCEL functions |
excel, dataset, proc step, macro definition, proc standard, standard deviation, data science, proc sql | source | 17 |
| 351 | Tying It All Together: A Story of Size Optimization at DSW | Linda Canada | Presentation | customer satisfaction, imputation, excel | source | 61 |
| 352 | Uncover the Most Common SAS Stored Process Errors | Patricia S. Aanderud, Angela M. Hall |
You don't have to be with the CIA to discover why your SAS stored process is producing clandestine results. In this talk, you will learn how to use prompts to get the results you want, work with the metadata to ensure correct results, and even pick up simple coding tricks to improve performance. You will walk away with a new decoder ring that allows you to discover the secrets of the SAS logs! | sas log, error message, proc printto, sas administrator, proc step | source | 7 |
| 353 | Uncovering Trends in Research Using SAS Text Analytics with Examples from Nanotechnology and Aerospace Engineering | Tom Sabo | Understanding previous research in key domains can help R&D organizations focus new research in non- duplicative areas and ensure that future endeavors do not repeat the mistakes of the past. However, manual analysis of previous research efforts can prove insufficient to meet these ends. This paper highlights how a combination of SAS Text Analytics and SAS Visual Analytics can deliver the capability to understand key topics and patterns in previous research and how this combination applies to a current research endeavor. We will explore these capabilities in two use cases. The first will be in uncovering trends in publicly visible, government-funded research (Small Business Innovation Research or SBIR) and how these trends apply to future research in nanotechnology. The second will be visualizing past research trends in publicly available NASA publications, and how these might impact the development of next-generation spacecraft | data mining, dataset, sas enterprise guide, correlation, monte carlo | source | 16 |
| 354 | Understanding and Applying the Logic of the DOW-Loop | Arthur X. Li | The DOW-loop is not official terminology that one can find in SAS documentation, but it was well-known and widely-used among experienced SAS programmers. The DOW-loop was developed over a decade ago by a few SAS gurus, including Don Henderson, Paul Dorfman, and Ian Whitlock. A common construction of the DOW-loop consists of a DO-UNTIL loop with a SET and a BY statement within the loop. This construction isolates actions that are performed before and after the loop from the action within the loop which results in eliminating the need for retaining or resetting the newly-created variables to missing in the DATA STEP. In this talk, in addition to understanding the DOW-loop construction, we will review how to apply the DOW-loop to various applications |
missing value, do loop, set statement, conditional do loop, retain, group by, if-then-else | source | 12 |
| 355 | Understanding Change in the Enterprise | Liz McIntosh, Nancy A. Rausch, Bryan Wolfe |
SAS provides a wide variety of products and solutions that address analytics, data management, and reporting. It can be challenging to understand how the data and processes in a SAS deployment relate to each other, and how changes in your processes affect downstream consumers. This paper presents visualization and reporting tools for lineage and impact analysis. These tools enable you to understand where the data for any report or analysis originates or how data is consumed by data management, analysis, or reporting processes. This paper introduces new capabilities to import metadata from third-party systems to provide lineage and impact analysis across your enterprise | data governance, sas management console | source | 14 |
| 356 | Unlock the Power of SAS Visual Analytics Starting with Multiple Microsoft Excel Files | Beena Mathew, Michelle Wilkie |
SAS Visual Analytics is a unique tool that provides both exploratory and predictive data analysis capabilities. As the visual part of the name suggests, the rendering of this analysis in the form of visuals (crosstabs, line charts, histograms, scatter plots, geo maps, treemaps, and so on) make this a very useful tool. Join me as I walk you down the path of exploring the capabilities of SAS Visual Analytics 6.3, starting with data stored in a desktop application as multiple Microsoft EXCEL files. Together, we import the data into SAS Visual Analytics, prepare the data using the data builder, load the data into SAS LASR Analytic Server, explore data, and create reports |
line chart, bar chart, correlation, proc freq, scenario analysis, excel, histogram | source | 28 |
| 357 | Up Your Game with Graph Template Language Layouts | Sanjay N. Matange | You have built the simple bar chart and mastered the art of layering multiple plot statements to create complex graphs like the survival plot using the SGPLOT procedure. You know all about how to use plot statements creatively to get what you need and how to customize the axes to achieve the look and feel you want. Now it's time to up your game and step into the realm of the "Graphics Wizard." Behold the magical powers of Graph Template Language layouts! Here you will learn the esoteric art of creating complex multi-cell graphs using LAYOUT LATTICE. This is the incantation that gives you the power to build complex, multi-cell graphs like the forest plot, stock plots with multiple indicators like MACD and Stochastic, Adverse Events by Relative Risk graphs, and more. If you ever wondered how the Diagnostics panel in the REG procedure was built, this paper is for you. Be warned, this is not the realm for the faint of heart! |
kaplan-meier plot, proc sgplot, scatter plot, class variable, forest plot, adverse events, histogram, bar chart, graph template language, box plot, relative risk | source | 20 |
| 358 | Useful Tips When Deploying SAS Code in a Production Environment | Elena Shtern | When deploying SAS code into a production environment, a programmer should ensure that the code satisfies the following key criteria: 1. The code runs without errors. 2. The code performs operations consistent with the agreed upon business logic. 3. The code is not dependent on manual human intervention. 4. The code performs necessary checks to provide sufficient quality control of the deployment process. Base SAS programming offers a wide range of techniques to support the last two aforementioned criteria. The presentation demonstrates the use of SAS macro variables in combination with simple macro programs to perform a number of routine automated tasks that are often part of the production-ready code. Some of the examples to be demonstrated include the following topics: How to check that required key parameters for successful program run are populated in the parameters file. How to automatically copy the content of the permanent folder to the newly created backup folder | proc step, macro variable, error message, quality control, sas library, proc sql, sas log | source | 6 |
| 359 | Using Arrays for Epidemic Modeling in SAS | Carl Grafe | Epidemic modeling is an increasingly important tool in the study of infectious diseases. As technology advances and more and more parameters and data are incorporated into models, it is easy for programs to get bogged down and become unacceptably slow. The use of arrays for importing real data and collecting generated model results in SAS can help to streamline the process so results can be obtained and analyzed more efficiently. This paper describes a stochastic mathematical model for transmission of influenza among residents and healthcare workers in long-term care facilities (LTCFs) in New Mexico. The purpose of the model was to determine to what extent herd immunity among LTCF residents could be induced by varying the vaccine coverage among LTCF healthcare workers | data science, proc mixed, array | source | 7 |
| 360 | Using Base SAS to Extend the SAS System | Mark L. Jordan | This paper demonstrates how to use Base SAS tools to add functional, reusable extensions to the SAS system. Learn how to do the following: Write user-defined macro functions that can be used inline with any other SAS code. Use PROC FCMP to write and store user-defined functions that can be used in other SAS programs. * Write DS2 user-defined methods and store them in packages for easy reuse in subsequent DS2 programs |
macro facility, proc step, permanent library, macro definition, sas base, proc fcmp, autocall macro, stored macro, sas function, options statement, macro put, macro function, sas log, today, %if-%then-%else, macro variable | source | 11 |
| 361 | Using Java to Harness the Power of SAS | Jeremy Palbicki | Are you a Java programmer who has been asked to work with SAS or a SAS programmer who has been asked to provide an interface to your IT colleagues? Let's face it, not a lot of Java programmers are heavy SAS users. If this is the case in your company, then you are in luck because SAS provides a couple of really slick features to allow Java programmers to access both SAS data and SAS programming from within a Java program. This paper walks beginner Java or SAS programmers through the simple task of accessing SAS data and SAS programs from a Java program. All that you need is a Java environment and access to a running SAS process, such as a SAS server. This SAS server can either be a SAS/SHARE server or an IOM server. However, if you do not have either of these two servers that is okay; with the tools that are provided by SAS, you can start up a remote SAS session within Java and harness the power of SAS |
dataset, flat file | source | 5 |
| 362 | Using Metadata-Bound Libraries to Authorize Access to SAS Data | Jack Wallace | Have you found OS file permissions to be insufficient to tailor access controls to meet your SAS data security requirements? Have you found metadata permissions on tables useful for restricting access to SAS data, but then discovered that SAS programmers can avoid the permissions by issuing LIBNAME statements that do not utilize the metadata? Would you like to ensure that users have access to only particular rows or columns in SAS data sets, no matter how they access the SAS data sets? Metadata-bound libraries provide the ability to authorize access to SAS data by authenticated Metadata User and Group Identities that cannot be bypassed by SAS programmers who attempt to avoid the metadata with direct LIBNAME statements. They also provide the ability to limit the rows and columns in SAS data sets that an authenticated user is allowed to see. The authorization decision is made in the bowels of the SAS I/O system, where it cannot be avoided when data is accessed |
sas management console, access control, dataset, proc copy, where statement, proc sql, create table, libname statement, alter table | source | 14 |
| 363 | Using Microsoft Windows DLLs within SAS Programs | Rajesh Lal | SAS has a wide variety of functions and call routines available. More and more Operating System level functionality has become available as part of SAS language and functions over the versions of SAS. However, there is a wealth of other Operating System functionality that can be accessed from within SAS with some preparation on the part of the SAS programmer. Much of the Microsoft Windows functionality is stored in easily re-usable system DLL (Dynamic Link Library) files. This paper describes some of the Windows functionality which may not be available directly as part of SAS language and methods of accessing that functionality from within SAS code. Using the methods described here, practically any Windows API should become accessible. User created DLL functionality should also be accessible to SAS programs | txt, proc step, data step | source | 10 |
| 364 | Using PROC GPLOT and PROC REG Together to Make One Great Graph | William 'Gui' Zupko II | Regression is a helpful statistical tool to show relationships between two or more variables. However, many users can find the barrage of numbers at best unhelpful, and at worst, undecipherable. Using the shipments and inventories' historical data from the U.S. Census Bureau's office of Manufacturers' Shipments, Inventories, and Orders' (M3) , we can create a graphical representation of two time series with PROC GPLOT and map out reported and expected results. By combining this output with results from PROC REG, we are able to highlight problem areas that may need a second look. The resulting graph shows which dates have abnormal relationships between our two variables and presents the data in an easy to use format which even users unfamiliar to SAS can interpret. This graph is ideal for analysts finding problematic areas such as outliers and trend-breakers or for managers to quickly discern complications and the effect they have on overall results |
proc step, proc reg, proc gplot, macro variable, regression analysis, outlier, proc printto | source | 14 |
| 365 | Using PROC MCMC for Bayesian Item Response Modeling | Yi-Fang Wu | Presentation | proc step, correlation | source | 2 |
| 366 | Using SAS Enterprise Miner to Predict the Injury Risk Involved in Car Accidents | Prateek Khare, Vandana Reddy, Goutam Chakraborty |
There are yearly 2.35 million road accident cases recorded in the U.S. Among them, 37,000 are considered fatal. Road crashes cost USD 230.6 billion per year, or an average of USD 820 per person. Our efforts are to identify the important factors that lead to vehicle collisions and to predict the injury risk involved in them. Data was collected from National Automotive Sampling System (NASS), containing 20,247 cases with 19 variables. Input variables describe the factors involved in an accident such as Height, Age, Weight, Gender, Vehicle model year, Speed limit, Energy absorption in Collision & Deformation location, etc. The target variable is nominal showing levels of injury. Missing values in interval variables were imputed using mean and class variables using the count method. Multivariate analysis suggests high correlation between tire footprint and wheelbase (Corr=0.97, P<0.0001) and original weight of car and curb weight of car (Corr=0.79, P<0.0001) | neural network, data mining, proc hpsplit, regression analysis, missing value, class variable, interval variable, data cleaning | source | 7 |
| 367 | Using SAS Graph Template Language with SAS 9.3 Updates to Visualize Data When There is Too Much Data to Visualize | Perry Watts, Nathaniel Derby |
Developing a good graph with ODS statistical graphics becomes a challenge when input data map to crowded dis- plays with overlapping points or lines. Such is the case with the Framingham Heart Study of 5209 subjects captured in the sashelp.heart data set, a series of 100 booking curves for the airline industry, and interleaving series plots that capture closing stock values over a twenty year period for three giants in the computer industry. In the paper, transparency, layering, data point rounding, and color coding are evaluated for their effectiveness to add visual clarity to graphics output. Version 9.2 compatible Graph Template Language (GTL) plotting statements referenced in the paper include HISTOGRAM, SCATTERPLOT, BOXPLOT, SERIESPLOT and BANDPLOT plus layouts OVERLAY, GRIDDED, DATAPANEL and LATTICE that produce single or multiple-panel graphs. GTL 9.3 updates have also been added to the paper |
scatter plot, histogram, series plot, time series analysis, proc step, graph template language, heatmap, proc template, proc freq, bar chart, proc means, macro variable, raw data, box plot | source | 20 |
| 368 | Using SAS MDM deployment in Brazil | Ielton de Melo Gonçalves | Dataprev has become the principal owner of social data on the citizens in Brazil by collecting information for over forty years in order to subsidize pension applications for the government. The use of this data can be expanded to provide new tools to aid policy and assist the government to optimize the use of its resources. Using SAS MDM, we are developing a solution that uniquely identifies the citizens of Brazil. Overcoming challenges with multiple government agencies and with the validation of survey records that suggest the same person requires rules for governance and a definition of what represents a particular Brazilian citizen. In short, how do you turn a repository of master data into an efficient catalyst for public policy? This is the goal for creating a repository focused on identifying the citizens of Brazil | data governance, data validation rules | source | 5 |
| 369 | Using SAS ODS Graphics | Chuck Kincaid | This presentation will teach the audience how to use SAS ODS Graphics. Now part of Base SAS, ODS Graphics are a great way to easily create clear graphics that allow any user to tell their story well. SGPLOT and SGPANEL are two of the procedures that can be used to produce powerful graphics that used to require a lot of work. The core of the procedures are explained, as well as the options available. Furthermore, we ex plore the ways to combine the individual statements to make more complex graphics that tell the story better. Any user of Base SAS on any platform will find great value from the SAS ODS Graphics procedures |
proc step, series plot, sas base, numeric variable, proc sgplot, proc sgpanel | source | 18 |
| 370 | Using SAS Software to Shrink the Data Used in Apache Flex Applications | Ahmed Al-Attar | This paper discusses the techniques I used at the Census Bureau to overcome the issue of dealing with large amounts of data while modernizing some of their public-facing web applications by using service oriented architecture (SOA) to deploy Flex web applications powered by SAS . The paper covers techniques that resulted in reducing 142,293 XML lines (3.6 MB) down to 15,813 XML lines (1.8 MB), a 50% size reduction on the server side (HTTP Response), and 196,167 observations down to 283 observations, a reduction of 99.8% in summarized data on the client side (XML Lookup file) | proc step, dataset | source | 8 |
| 371 | Using SAS Stored Processes To Build a Calibration Tool | Anita Measey | Presentation | macro variable, sas base, proc report, raw data, proc step, xls, dataset | source | 41 |
| 372 | Using SAS to Analyze the Impact of the Affordable Care Act | John J. Cohen, Meenal Sinha |
The Affordable Care Act that is being implemented now is expected to fundamentally reshape the health care industry. All current participants --providers, subscribers, and payers --will operate differently under a new set of key performance indicators (KPIs). This paper uses public data and SAS software to illustrate an approach to creating a baseline for the health care industry today so that structural changes can be measured in the future to establish the impact of the new laws | sample, data visualization, dataset, proc freq, proc step | source | 12 |
| 373 | Using SAS to Evaluate Patient-Directed Quality of Care Interventions | Scott P. Leslie | Health plans use wide-ranging interventions based on criteria set by nationally recognized organizations (e.g., NCQA, CMS) to change health-related behavior in large populations. Evaluation of these interventions has become more important with the increased need to report patient-centered quality of care outcomes. Findings from evaluations can detect successful intervention elements and identify at-risk patients for further targeted interventions. This paper describes how SAS was applied to evaluate the effectiveness of a patient-directed intervention designed to increase medication adherence and a health plan's CMS Part D Star Ratings. Topics covered include querying data warehouse tables, merging pharmacy and eligibility claims, manipulating data to create outcome variables, and running statistical tests to measure pre-post intervention differences | proc step, odds ratio, proc glm, predictor variable, proc genmod, data warehouse, proc logistic, proc tabulate, confidence interval, logistic distribution, proc sql | source | 10 |
| 374 | Using SAS to Examine Internal Consistency and to Develop Community Engagement Scores | Renee Gennarelli, Melody S. Goodman |
Comprehensive cancer centers have been mandated to engage communities in their work; thus, measurement of community engagement is a priority area. Siteman Cancer Center's Program for the Elimination of Cancer Disparities (PECaD) seek to align projects with 11 Engagement Principles (EP) previously developed in the literature. Participants in a PECaD pilot project were administered a survey with questions on community engagement in order to evaluate how well the project aligns with the EPs. Internal consistency is examined using PROC CORR with the ALPHA option to calculate Cronbach's alpha for questions that relate to the same EP. This allows items that have a lack of internal consistency to be identified and to be edited or removed from the assessment. EP-specific scores are developed on quantity and quality scales. Lack of internal consistency was found for six of the EP's (alpha<.70). After editing the items, all EP question groups had strong internal consistency (alpha>.85) |
proc princomp, proc step, proc corr, proc factor, sample size, correlation, sample, missing value, standard deviation, sensitivity analysis, proc freq | source | 10 |
| 375 | Using SAS to Get More for Less | Faron Kincheloe | Especially in this current financial climate, many of us are being asked to do more with less. For several years, the Office of Institutional Research and Testing (IRT) at Baylor University has been using SAS software to increase the efficiency of the office and of the University as a whole. Reports that were once prepared manually have been automated. Data quality processes have been implemented in order to reduce the number of duplicate mailings. Predictive modeling is used to focus recruiting efforts on those prospective students most likely to respond. A web- based portal has been created to provide self-service report generation for many administrators across campus. Along with this, a number of data processing functions have been centralized, eliminating the need for additional programming skills and software support. This presentation discusses these improvements in more detail and provides examples of the end results | excel, data cleaning | source | 5 |
| 376 | Using SAS to Support the Implementation of a Patient-Centered Outcomes Research Institute Grant Funded by the Affordable Care Act | Michele Mesiano, Meghna Parthasarathy, Lauren Terhorst |
The Patient-Centered Outcomes Research Institute (PCORI) was created as part of the Affordable Care Act. PCORI is authorized by Congress to conduct research to provide information about the best available evidence to help patients and their health care providers make more informed decisions. Community Care Behavioral Health Organization in Pittsburgh, Pennsylvania was awarded a PCORI research grant to investigate health care system improvements for adults with serious mental illness. The grant, titled "Optimizing Behavioral Health Homes by Focusing on Outcomes that Matter Most for Adults with Serious Mental Illness," began in January of 2013 and is ongoing. Information Technology staff at Community Care have leveraged SAS solutions in providing real-time data extraction and reports to support the development and implementation of this research project. SAS tools have been used to merge data from multiple platforms and database sources, including web data sources | proc report, excel, treatment arm, proc step, ods html | source | 12 |
| 377 | Using SAS/STAT Software to Validate a Health Literacy Prediction Model in a Primary Care Setting | Lucy D'Agostino McGowan, Melody S. Goodman, Kimberly A. Kaphingst |
Existing health literacy assessment tools developed for research purposes have constraints that limit their feasibility for use in clinical settings. The measurement of health literacy in clinical practice can be impractical due to the staff burden and time requirements of existing assessment tools. Single Item Literacy Screener (SILS) items, which are self-administered brief screening questions, have been developed to address this constraint. We developed a model to predict limited health literacy that consists of two SILS and demographic information (age, gender, race, and education) using a sample of patients in a St. Louis emergency department. In this paper, we validate this prediction model in a separate sample of patients visiting a safety net primary care clinic in St. Louis. Using the prediction model developed in the previous study, we use SAS/STAT software to validate this model based on two goodness of fit criteria: rescaled R-squared and AIC |
sample, roc curve, cohen's kappa, goodness of fit, correlation, pearson correlation | source | 7 |
| 378 | Using Sorting Algorithms to Create Sorted Lists | Matthew Neft, Chelle Pronko |
When providing lengthy cost and utilization data to medical providers it is ideal to sort the report by descending cost (or utilization) so that the high cost / utilized categories are at the top. This task can be easily solved using PROC SORT. However, when your data is listed out horizontally and you need other variables (such as unit cost per procedure or national average) to follow the sort but not be sorted themselves the solution is not as intuitive. This paper looks at two sorting algorithms to solve this problem. First, we look at the basic bubble sort (which is effective for small data) that sets up arrays for each variable and then sorts on just one of them. Next, we discuss the quicksort algorithm which is effective for large data sets. The results of the sorts provide sorted data that can be easily manipulated and put into reports using DDE or ODS |
do loop, cpu time, proc sort | source | 5 |
| 379 | Validating Self-Reported Survey Measures Using SAS | Sarah Lyons, Kimberly Kaphingst, Melody S. Goodman |
Researchers often rely on self -reported responses for survey research studies. The accuracy of this self -reported data is often unknown, particularly in a safety-net medical setting that serves a patient population with varying levels of health literacy. We recruited participants from the waiting room of a St. Louis primary care safety net clinic to participate in a survey investigating the relationship between health environments , health behavior s and health outcomes. The survey included questions regarding personal and family history of chronic disease (diabetes, heart disease, and cancer) and self -reported height and weight (used to calculate Body Mass Index) . We subsequently accessed the partic ipant's electronic medical record (EMR) and collected physician-reported data on the same variables. We calculated concordance rates between participant answers and information gathered from EMRs using McNemar's test | proc freq, proc step, logistic distribution, logistic regression, proc logistic, response variable, regression analysis | source | 6 |
| 380 | VFORMAT Lets SAS Do the Format Searching | Peter Crawford | When reading data files or writing SAS programs, we are often hunting for the right format or informat. There are so many to choose from! Does it seem like too many to search the manual? Let SAS help find the right one! We use the SAS dictionary table VFORMAT and a very small SAS program. This presentation demonstrates how two simple functions unlock the potential of this great resource: SASHELP.VFORMAT | proc sql, sample, dictionary tables, sas programmer | source | 6 |
| 381 | Visualizing Lake Michigan Wind with SAS Software | Aaron Clark | A wind resource assessment buoy, residing in Lake Michigan, uses a pulsing laser wind sensor to measure wind speed and direction offshore up to a wind turbine hub-height of 175m and across the blade span every second. Understanding wind behavior would be tedious and fatiguing with such large data sets. However, SAS/GRAPH 9.4 helps the user grasp wind characteristics over time and at different altitudes by exploring the data visually. This paper covers graphical approaches to evaluate wind speed validity, seasonal wind speed variation, and storm systems to inform engineers about the energy potential of Lake Michigan offshore wind farms |
proc step, rolling window, proc sgplot, proc freq | source | 8 |
| 382 | Volatility Estimation through ARCH/GARCH Modeling | Aric LaBarr | Volatility estimation plays an important role in the fields of statistics and finance. Many different techniques address the problem of estimating volatility of financial assets. Autoregressive conditional heteroscedasticity (ARCH) models and the related generalized ARCH models are popular models for volatility. This paper will introduce the need for volatility modeling as well as introduce the framework of ARCH and GARCH models. A brief discussion about the structure of ARCH and GARCH models will then be compared to other volatility modeling techniques | normal distribution, residual | source | 5 |
| 383 | Washing the Elephant: Cleansing Big Data Without Getting Trampled | Mike Frost | Data quality is at the very heart of accurate, relevant, and trusted information, but traditional techniques that require the data to be moved, cleansed, and repopulated simply can't scale up to cover the ultra-jumbo nature of big data environments. This paper describes how SAS Data Quality Accelerators for databases like Teradata and Hadoop deliver data quality for big data by operating in situ and in parallel on each of the nodes of these clustered environments. The paper shows how data quality operations can be easily modified to leverage these technologies. It examines the results of performance benchmarks that show how in-database operations can scale to meet the demands of any use case, no matter how big a big data mammoth you have | data cleaning, big data, stored procedure, data warehouse, proc sql | source | 8 |
| 384 | Weighted Methods for Analyzing Missing Data with the GEE Procedures | Guixian Lin, Bob Rodriguez |
Missing observations caused by dropouts or skipped visits present a problem in studies of longitudinal data. When the analysis is restricted to complete cases and the missing data depend on previous responses, the generalized estimating equation (GEE) approach, which is commonly used when population-averaged effects are of primary interest, can lead to biased parameter estimates. The new GEE procedure in SAS/STAT 13.2 implements a weighted GEE method, which provides consistent parameter estimates when the dropout mechanism is correctly specified. When none of the data are missing, the method is identical to the usual GEE approach, which is available in the GENMOD procedure. This paper reviews the concepts and statistical methods for weighted GEE and illustrates them with an example |
proc step, regression analysis | source zip |
8 |
| 385 | What Benefits Can a Hospital Achieve by an Automated Structured Search in Medical Records for Mapping of Patient Injuries? | Tonje Hansen | Nordland Hospital Trust has made considerable progress in extracting valuable insights from data in electronic patient journals. Nordland Hospital Trust provides specialist health services for a population of more than 200,000 in the far north of Norway, where the distances are vast and treatment centers are few and far between. Since 2010 Nordland Hospital Trust has used the Global Trigger Tool (GTT) systematically in identifying adverse events in patient records. The GTT method is a retrospective review of a random sample of inpatient hospital records searching for "triggers" (or indicators of potential patient harm) to identify possible adverse events. Triggers range from being treated for an infection to suffering a pulmonary embolism while admitted to the hospital | adverse events, unstructured data | source | 4 |
| 386 | What You're Missing About Missing Values | Christopher J. Bost | Do you know everything you need to know about missing values? Do you know how to assign a missing value to multiple variables with one statement? Can you display missing values as something other than . or blank? How many types of missing numeric values are there? This paper reviews techniques for assigning, displaying, referencing, and summarizing missing values for numeric variables and character variables | missing value, numeric variable, character variable, proc step, proc freq, proc format, call missing, format statement, proc print, character functions | source | 11 |
| 387 | What's New in SAS Data Management | Nancy A. Rausch, Mike Frost, Michael Ames |
The latest releases of SAS Data Integration Studio and SAS Data Management 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. The latest features include: capabilities for building complex job processes web and tablet environments for managing your data enhanced ELT transformation capabilities big data transformation capabilities for Hadoop integration with the SAS LASR platform enhanced features for lineage tracing and impact analysis * new features for 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 product capabilities | data cleaning, hadoop, big data, data dictionary, data integration, data governance | source | 15 |
| 388 | What's New in SAS Enterprise Miner 13.1 | Jared Dean, Jonathan Wexler |
Over the last year, the SAS Enterprise Miner development team has made numerous and wide-ranging enhancements and improvements. New utility nodes that save data, integrate better with open-source software, and register models make your routine tasks easier. The area of time series data mining has three new nodes. There are also new models for Bayesian network classifiers, generalized linear models (GLMs), support vector machines (SVMs), and more | data mining, time series analysis, proc princomp, correlation, proc step, dimensionality reduction, proc hpforest, proc genmod, data cleaning, big data, support vector machine, machine learning, feature selection, insurance claims, proc adaptivereg, seasonal decomposition, sample, score code | source | 16 |
| 389 | What's on My Mainframe? A Macro That Gives You a Solid Overview of Your SAS Data on z/OS | Jesper Michelsen | In connection with the consolidation of our SAS platform at Nykredit the data stored on the Nykredit z/OS SAS installation had to be migrated (copied) to the new x64 Windows SAS platform storage. However, getting an overview of these data on the z/OS mainframe can be difficult, and a series of questions arise during the process. For example: Who is responsible? How many bytes? How many rows and columns? When were the data created? And so on. With extensive use of FILENAME FTP and looping, and extracting metadata, it is possible to get an overview of the data on the host presented in an EXCEL sheet |
filename statement, %if-%then-%else, macro loop, macro variable, excel, create table | source | 7 |
| 390 | When Do You Schedule Preventative Maintenance? Multivariate Time Series in SAS/ETS | Kenneth Sanford | Expensive physical capital must be regularly maintained for optimal efficiency and long-term insurance against damage. The maintenance process usually consists of constantly monitoring high-frequency sensor data and performing corrective maintenance when the expected values do not match the actual values. An economic system can also be thought of as a system that requires constant monitoring and occasional maintenance in the form of monetary or fiscal policy. This paper shows how to use the SSM procedure in SAS/ETS to make forecasts of expected values by using high-frequency multivariate time series. The paper also demonstrates the functionality of the new SASEFRED interface engine in SAS/ETS |
time series analysis, proc freq, time series data, regression analysis, covariance, proc step | source | 8 |
| 391 | Where in the World Are SAS/GRAPH Maps? An Exploration of the Old and New SAS Mapping Capacities | Louise S. Hadden | SAS has an amazing arsenal of tools to use and display geographic information that is relatively unknown and underutilized. This presentation highlights both new and existing capacities for creating stunning, informative maps as well as using geographic data in other ways. SAS provided map data files, functions, format libraries and other geographic data files are explored in detail. Custom mapping of geographic areas are discussed. Maps produced include use of both the annotate facility (including some new functions) and PROC GREPLAY. Products used are Base SAS and SAS/GRAPH. SAS programmers of any skill level will benefit from this presentation |
macro variable, proc step | source | 16 |
| 392 | Work Area Optimization at a Major European Utility Company | Jinxin Yi, Emily Lada, Anne Smith, Colin Gray |
A European utility company has several thousand service engineers who provide its customers with services that range from performing routine maintenance to handling emergency breakdowns. Each service engineer is assigned to a work area that consists of a set of postal sectors. The company wants to understand how it should configure its work areas to improve customer satisfaction, minimize travel time for its full-time service engineers, and minimize the costs of overtime and subcontractor hours. This paper describes the use of SAS/OR optimization procedures to model this problem and configure optimal work areas, and the use of SAS Simulation Studio to simulate how the optimal configurations might satisfy the customer service requirements. The experimental results show that the proposed solution can satisfy customer demand within the desired service- time window, with significantly less travel time for the engineers, and with lower overtime and subcontractor costs |
dataset, customer satisfaction | source | 6 |
| 393 | Working with Character Data | Andrew T. Kuligowski, Swati Agarwal |
The DATA STEP allows one to read, write, and manipulate many types of data. As data evolves to a more free- form state, SAS's ability to handle character data becomes increasingly important. This presentation will address character data from multiple vantage points. For example, what is the "default" length of a character string, and why does it appear to change under different circumstances? What sort of formatting is available to us for character data? How can we examine and manipulate character data? This paper will aim at a beginning to intermediate audience, and will stress an introduction to the numerous character functions available in SAS, including the basic LENGTH, and SUBSTR, plus many others |
character variable, length statement, character functions, macro variable, upcase function | source | 8 |
| 394 | Wow, I Could Have Had a VA! - A Comparison Between SAS Visual Analytics and Other SAS Products | Brian K. Varney | SAS Visual Analytics (VA) is one of the newer SAS products with a lot of excitement surrounding it. But what is SAS VA really? By examining the similarities, differences, and synergies between SAS VA and other SAS products, we can more clearly understand SAS VA | sas visual analytics, high-performance sas, data mining | source | 7 |
| 395 | You Can Have It All: Building Cumulative Data sets | Myra A. Oltsik | We receive daily files from a vendor which are updated every day so that we have the most current information. The problem with this kind of file is that valuable pieces of information can change over time, so that historical information is lost. Whether it's an address, school, supplier, status of an order, length of time to deliver something, etc., this type of file can only be used to analyze what's happening now. But what if you wanted to capture information that changed? To avoid losing these changes, you have to build a cumulative dataset. I'll show you how to build it | proc step, proc sql, macro variable, order by, txt, proc sort | source | 18 |
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