SAS Statistics and Operations Research May 2019

I hope those of you who attended SAS^® Global Forum in Dallas had as wonderful a time as I did. The conference did not disappoint, from Dr. Goodnight’s opening remarks to Dr. Michio Kaku’s closing session on the future of analytics. I did get to see a few outstanding paper sessions, but I spent most of my time in the Quad. I think everyone who visited the demo area would agree that it was Texas big! I had quite a nice crowd for my talk on scoring using SAS/STAT^® procedures. The R&D super demos, held in the Quad, also attracted good crowds. Hope to see all of you again at next year’s conference in Washington, DC.

The PLM procedure is one of my favorites. If you are not familiar with this procedure and what it can do for you after you fit a model, check out Rick Wicklin’s post below on using PROC PLM with linear regression models. It should peak your interest and get you excited to learn more about this procedure.

SAS/STAT is no longer the only place to look for new statistical tools in SAS. SAS^® Visual Statistics is also marching ahead, with new analytic components for mixed models, model-based clustering, and independent component analysis. These components join a suite of tools for descriptive and predictive modeling, regression, and clustering. For more information, see the documentation for SAS Visual Statistics Procedures.

Now that SAS Global Forum is behind us, preparations have begun in earnest for the Joint Statistical Meetings in Denver at the end of July. SAS will be well represented in Professional Development sessions. Check out our offerings in both Continuing Education courses and Computer Technology Workshops. And as always, come see us at the SAS booth!

Phil Gibbs

Manager, Advanced Analytics Technical Support

Technical Papers

Building a Propensity Score Model with SAS/STAT Software: Planning and Practice

Learn how you can use the PSMATCH procedure in conjunction with other procedures in SAS/STAT software to tackle some practical challenges. In particular, the paper demonstrates how you can use causal graphs to investigate questions related to ignorability and how you can incorporate propensity scores that are computed using approaches other than logistic regression. The paper also illustrates features of PROC PSMATCH that you can use to try to improve covariate balance and control properties of the final matched data set.

Optimization Modeling with Python and SAS^® Viya^®

Read about an approach for Python programmers to naturally model their optimization problems, solve them by using SAS^® Optimization solver actions, and view and interact with the results. The common tools for using the optimization solvers in SAS for these purposes are the OPTMODEL and IML procedures, but programmers more familiar with Python might find this alternative approach easier to grasp.

Technical Highlights

Lights, Camera, Action!

Tips and Techniques for Running CAS Actions

Robert Cohen presents a hands-on demo of tips and tricks for running SAS^® Cloud Analytic Server (CAS) actions by simulating data that correspond to a ciabatta recipe from a popular television baking show.

Power Analysis for Generalized Linear Models Using the New CUSTOM Statement in PROC POWER

John Castelloe demonstrates how you can use the CUSTOM statement in PROC POWER (released in SAS/STAT 14.2) to compute power and sample size for generalized linear models such as Poisson regression, logistic regression, and zero-inflated models. It works in concert with an exemplary data set—one that you create to represent your conjectures about the population you are studying—and the SAS/STAT procedure that you plan to use for the eventual data analysis.

Didn’t Get to Attend SAS Global Forum This Year?

Not to worry. The SAS Global Forum 2019 Proceedings are available now from our website. Explore the array of educational and inspiring sessions that were offered this year. Also available are on-demand videos of the opening session, technology connection, and more.

The DO Loop

Distinguished Research Statistician Developer Rick Wicklin shows four ways to use PROC PLM to obtain results from your regression model, an easier way to perform regression with restricted cubic splines in SAS, and examples of using the “NormalMix” distribution in SAS, along with a trick that enables you to easily work with distributions that have many components.

Tech Support Points Out

Area under the ROC Curve Measure (AUC) for Multinomial Models

The area under the ROC curve (AUC) is a widely used measure of model performance for binary-response models such as logistic models. Hand and Till (2001) proposed an extension to this measure for responses with more than two classes by averaging pairwise comparisons. Their multinomial measure reduces to the usual AUC when the response is binary. The MultAUC macro implements this extended measure. Several examples are given, assessing the AUC for models from multiple methods and procedures including logistic regression, discriminant analysis, classification tree analysis, and model-based cluster analysis.

Talks and Tutorials

JSM 2019

Registration is now open.
July 27 - August 1
Colorado Convention Center
Denver, CO

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