September 7-9, 2016      
WESTERN USERS OF SAS SOFTWARE                                                                       




SAS Presenters 2016

Fang K. Chen
Fang Chen is a Senior Manager of Bayesian Statistical Modeling in Advanced Analytics Division at SAS Institute Inc. Among his responsibilities are development of Bayesian analysis software and MCMC procedure. Prior to joining SAS Institute, he received his PhD in statistics from Carnegie Mellon University in 2004.
Bayesian Analysis using the MCMC Procedure
The MCMC procedure is a general-purpose Markov chain Monte Carlo simulation tool designed to fit Bayesian models. It uses a variety of sampling algorithms to generate samples from the targeted posterior distributions. This tutorial reviews the methods available with PROC MCMC and demonstrate its use with a series of real-world applications that increase in complexity. Examples include fitting a variety of parametric models: generalized linear models, linear and nonlinear models, hierarchical models, zero-inflated Poisson models, and missing data problems. Recent releases introduced major enhancements to the MCMC procedure that allow it to fit a richer collection of Bayesian models, and this tutorial describes how to perform analyses such as autoregressive models and PK/PD models. Additional Bayesian topics such as sensitivity analysis, inference of functions of parameters, and usage of historical data will also be discussed.

This tutorial is intended for statisticians who are interested in Bayesian computation. A basic understanding of Bayesian methods is assumed (and not reviewed). Attendees should have experience programming with the SAS language.

Fitting Your Favorite Mixed Models with PROC MCMC
The popular MIXED, GLIMMIX, and NLMIXED procedures in SAS/STAT® software fit linear, generalized linear, and nonlinear mixed models, respectively. These procedures take the classical approach of maximizing the likelihood function to estimate model parameters. The flexible MCMC procedure in SAS/STAT can fit these same models by taking a Bayesian approach. Instead of maximizing the likelihood function, PROC MCMC draws samples (using a variety of sampling algorithms) to approximate the posterior distributions of model parameters. Similar to the mixed modeling procedures, PROC MCMC provides estimation, inference, and prediction.This paper describes how to use the MCMC procedure to fit Bayesian mixed models and compares the Bayesian approach to how the classical models would be fit with the familiar mixed modeling procedures. Several examples illustrate the approach in practice.
Jane Eslinger
Jane Eslinger is a Technical Support Analyst at SAS Institute Inc., in Cary, NC. She supports the REPORT procedure, ODS, and other Base SAS procedures. Before she joined SAS, Jane worked as a statistical programmer in the social science and clinical research fields. She is a graduate of NC State University with a Bachelor of Science in Statistics.
The Dynamic Duo: ODS Layout and the ODS Destination for PowerPoint
Like a good pitcher and catcher in baseball, ODS layout and the ODS destination for PowerPoint are a winning combination in SAS® 9.4. With this dynamic duo, you can go straight from performing data analysis to creating a quality presentation. The ODS destination for PowerPoint produces native PowerPoint files from your output. When you pair it with ODS layout, you are able to dynamically place your output on each slide.Through code examples this paper shows you how to create a custom title slide, as well as place the desired number of graphs and tables on each slide. Don’t be relegated to the sidelines––increase your winning percentage by learning how ODS layout works with the ODS destination for PowerPoint.
The REPORT Procedure: A Primer for the Compute Block
It is well-known in the world of SAS® programming that the REPORT procedure is one of the best procedures for creating dynamic reports. However, you might not realize that the compute block is where all of the action takes place! Its flexibility enables you to customize your output.This paper is a primer for using a compute block. With a compute block, you can easily change values in your output with the proper assignment statement and add text with the LINE statement. With the CALL DEFINE statement, you can adjust style attributes such as color and formatting. Through examples, you learn how to apply these techniques for use with any style of output. Understanding how to use the compute-block functionality empowers you to move from creating a simple report to creating one that is more complex and informative, yet still easy to use.
I-Kong Fu
I-Kong is a product manager at SAS and is interested in analytics applied to marketing and sports, and learning about machine learning. He enjoys meeting SAS users at SAS conferences and has also helped organize various analytics related events and meetings in the Triangle area of North Carolina, USA.
Bringing Google Analytics, Facebook, and Twitter Data to SAS® Visual Analytics
Your marketing team would like to pull data from its different marketing activities into one report. What happens in Vegas might stay in Vegas, but what happens in your data does not have to stay there, locked in different tools or static spreadsheets. Learn how to easily bring data from Google Analytics, Facebook, and Twitter into SAS® Visual Analytics to create interactive explorations and reports on this data along with your other data for better overall understanding of your marketing activity.
Chevell Parker
Chevell Parker is ​a member of the Foundation SAS team in Technical Support. His main areas of support include The Output Delivery System and XML Technologies. Chevell joined SAS in 1993.
A Ringside Seat: The ODS Excel Destination versus the ODS ExcelXP Tagset
The new and highly anticipated ODS destination for Microsoft Excel is finally here! Available as a production feature in the third maintenance release of SAS® 9.4 (TS1M3), this new destination generates native Excel (XLSX) files that are compatible with Microsoft Office 2010 or later. This paper is written for anyone, from entry-level programmers to business analysts, who use the SAS® System and Microsoft Excel to create reports.The discussion covers features and benefits of the new Excel destination, differences between the Excel destination and the older ExcelXP tagset, and functionality that exists in the ExcelXP tagset that is not available in the Excel destination. These topics are all illustrated with meaningful examples.The paper also explains how you can bridge the gap that exists as a result of differences in the functionality between the destination and the tagset. In addition, the discussion outlines when it is beneficial for you to use the Excel destination versus over the ExcelXP tagset, and vice versa. After reading this paper, you should be able to make an informed decision about which tool best meets your needs.
Robert Rodriguez
Bob Rodriguez is a senior director in SAS Research & Development with responsibility for the development of statistical software, including SAS/STAT and SAS/QC.  He received his PhD in statistics from the University of North Carolina at Chapel Hill and worked as a research statistician at General Motors Research Laboratories before joining SAS in 1983.  Bob is a Fellow of the American Statistical Association.
Introducing the HPGENSELECT Procedure: Model Selection for Generalized Linear Models and More
Generalized linear models are highly useful statistical tools in a broad array of business applications and scientific fields. How can you select a good model when numerous models that have different regression effects are possible? The HPGENSELECT procedure, which was introduced in SAS/STAT® 12.3, provides forward, backward, and stepwise model selection for generalized linear models. In SAS/STAT 14.1, the HPGENSELECT procedure also provides the LASSO method for model selection. You can specify common distributions in the family of generalized linear models, such as the Poisson, binomial, and multinomial distributions. You can also specify the Tweedie distribution, which is important in ratemaking by the insurance industry and in scientific applications. You can run the HPGENSELECT procedure in single-machine mode on the server where SAS/STAT is installed. With a separate license for SAS® High-Performance Statistics, you can also run the procedure in distributed mode on a cluster of machines that distribute the data and the computations. This paper shows you how to use the HPGENSELECT procedure both for model selection and for fitting a single model. The paper also explains the differences between the HPGENSELECT procedure and the GENMOD procedure.
Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT® Software
The increasing size and complexity of data in research and business applications require a more versatile set of tools for building explanatory and predictive statistical models. In response to this need, SAS/STAT® software continues to add new methods. This presentation takes you on a high-level tour of five recent enhancements: new effect selection methods for regression models with the GLMSELECT procedure, model selection for generalized linear models with the HPGENSELECT procedure, model selection for quantile regression with the HPQUANTSELECT procedure, construction of generalized additive models with the GAMPL procedure, and building classification and regression trees with the HPSPLIT procedure. For each of these approaches, the presentation reviews its key concepts, uses a basic example to illustrate its benefits, and guides you to information that will help you get started.
Brett Wujek
Dr. Brett Wujek is a Senior Data Scientist with the R&D team in the SAS Advanced Analytics division. He helps evangelize and guide the direction of advanced analytics development at SAS, particularly in the areas of machine learning and data mining. Prior to joining SAS, Dr. Wujek led the development of process integration and design exploration technologies at Dassault Systemes, helping to architect and implement industry-leading computer-aided optimization software for product design applications. His formal background is in design optimization methodologies, receiving his PhD from the University of Notre Dame for his work developing efficient algorithms for multidisciplinary design optimization.
Best Practices in Machine Learning Applications
Building representative machine learning models that generalize well on future data requires careful consideration both of the data at hand and of assumptions about the various available training algorithms. Data are rarely in an ideal form that enables algorithms to train effectively. Some algorithms are designed to account for important considerations such as variable selection and handling of missing values, whereas other algorithms require additional preprocessing of the data or appropriate tweaking of the algorithm options. Ultimate evaluation of a model’s quality requires appropriate selection and interpretation of an assessment criterion that is meaningful for the given problem. This paper discusses the most common mistakes that machine learning practitioners make and provides guidance for using these powerful algorithms to build effective models.
Cynthia Zender
Cynthia Zender is a Technical Trainer and Curriculum Consultant for SAS Education. She teaches the Base Programming courses, the Report and Graph courses and many of the Business Intelligence courses. Cynthia is the co-author, with Lauren Haworth Lake and Michele Burlew of the book ODS: The Basics and Beyond.
Cynthia lives in Chicago with her husband.
Cynthia Zender
This 3-4 hour seminar is based on the SAS Global Forum paper of the same name ( ). This seminar is for intermediate SAS programmers. In the seminar, we will investigate how eight (8) complex reports were produced with SAS. All the code that produced the reports will be covered, in detail. All report output is produced using ODS (rather than LISTING) output. The reports to be covered include three versions of a standard demographic report, producing a color-banded report with PROC TABULATE, producing a report which uses special fonts (Bissantz SparkFonts) to produce a sparkline report, several graph examples and several unique report ordering examples. Procedures/Topics to be covered include: REPORT, TABULATE, FORMAT, MEANS, FREQ, Macro processing and DATA _NULL_ programming (as used to produce the reports) . Refer to the SAS Global Forum paper to see the actual reports which will be discussed in detail.
Macro Basics for New SAS® Users
Are you new to SAS®? Do you look at programs written by others and wonder what those & and % signs mean? Are you reluctant to change code that has macro variables in the program? Do you need to perform repetitive programming tasks and don’t know when to use DO versus %DO?This paper provides an overview of how the SAS macro facility works and how you can make it work in your programs. Concrete examples answer these and other questions: Where do the macro variables live? What does it mean when I see multiple ampersands (&&)? What is a macro program, and how does it differ from other SAS programs? What’s the big difference between DO and IF and %DO and %IF?
This workshop provides an overview of the TAGSETS.EXCELXP destination. Then, concrete examples of using TAGSETS.EXCELXP will be demonstrated. Programs shown will include: using the ExcelXP suboption list; getting the ExcelXP internal help file to display in the SAS log; creating and naming multiple worksheet files; controlling when a new sheet starts; setting column width defaults; using an INI file to set defaults such as orientation or zoom level; sending Excel formats from SAS so that leading zeroes or decimal places are shown when the file opens in Excel, and by group processing will be discussed. In addition, some style templates designed specifically for the ExcelXP destination will be demonstrated.