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SAS Statistics and Operations Research News

Greetings to all who survived the warm summer. And best of luck to those about to begin their summer season. 

We’ve just released our first two products on the new SAS® Viya™ platform, Visual Data Mining and Machine Learning and SAS® Visual Investigator. Take a look!

Note that you have just a few more days to submit your paper proposal for the 2017 SAS® Global Forum in Orlando on April 2–5. The call for papers ends and registration begins October 3. If you are faculty or students, scholarships are available, with an application deadline of January 13, 2017.

In between baseball playoff games, I’ll be teaching a tutorial on modeling longitudinal categorical response data at the 2016 MidWest SAS® Users Group (MWSUG) conference in Cincinnati on October 9–11.

Finally, if you have a few minutes, we’d love to have you take a look at beta pages on the support.sas.com website and give us your feedback.

Maura Stokes

Senior R&D Director, Statistical Applications 

 

Technical Papers

 

Fitting Multilevel Hierarchical Mixed Models Using PROC NLMIXED

Hierarchical nonlinear mixed models are complex models that occur naturally in many fields. The NLMIXED procedure’s ability to fit linear and nonlinear models with standard or general distributions enables you to fit a wide range of such models. SAS/STAT® 13.2 enhanced PROC NLMIXED to support multiple RANDOM statements, enabling you to fit nested multilevel mixed models. This paper uses an example to illustrate the new functionality.

 

Highly Customized Graphs Using ODS Graphics

You can use annotation, modify templates, and change dynamic variables to customize graphs in SAS. Standard graph customization methods include template modification (which most people use to modify graphs that analytical procedures produce) and SG annotation (which most people use to modify graphs that procedures such as PROC SGPLOT produce). However, you can also use SG annotation to modify graphs that analytical procedures produce. You begin by using an analytical procedure, ODS Graphics, and the ODS OUTPUT statement to capture the data that go into the graph. You use the ODS document to capture the values that the procedure sets for the dynamic variables, which control many of the details of how the graph is created. You can modify the values of the dynamic variables, and you can modify graph and style templates. Then you can use PROC SGRENDER along with the ODS output data set, the captured or modified dynamic variables, the modified templates, and SG annotation to create highly customized graphs. This paper shows you how and provides examples.

 

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SAS/STAT Users Still Moving to the SAS 9.4 Platform?

Aren’t we all! We featured this handout last time, but it’s timeless!

If you are moving up to SAS 9.4 and would like to catch up on the recent SAS/STAT releases on that platform, this handout is for you! Get an overview of our new additions in missing data analysis, modern survival data analysis, statistical modeling, spatial point pattern analysis, Bayesian analysis, item response analysis, classification and regression trees, and performance enhancements. There’s truly something here for everyone. And if you aren’t currently on the move, feel free to use this handout however it helps you get into the passing lane!

 

Using the OPTMODEL Procedure in SAS/OR® to Find the k Best Solutions

Because optimization models often do not capture some important real-world complications, a collection of optimal or near-optimal solutions can be useful for decision makers. This paper uses various techniques for finding the k best solutions to the linear assignment problem in order to illustrate several features recently added to the OPTMODEL procedure in SAS/OR software. These features include the network solver, the constraint programming solver (which can produce multiple solutions), and the COFOR statement (which allows parallel execution of independent solver calls).

 

Technical Highlights

 
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The DO Loop

Distinguished Research Statistician Developer Rick Wicklin shows you how to compute bootstrap confidence intervals in SAS and follows that up with a posting on smooth bootstrap methods. If you want to spruce up your graphics, learn about coloring markers in a scatter plot so that the colors indicate the value of a continuous third variable. Or create animation by taking advantage of the BY statement in the SGPLOT procedure.

Wicklin’s blog has accumulated a wealth of information over the years, all of which is relevant. I just tried a bunch of different terms and was not disappointed!

 

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Speaking of Graphics

Distinguished Research Statistician Developer Warren Kuhfeld has written two posts on the Graphically Speaking blog. You can learn about controlling the order of legend entries and the assignment of groups to style elements in PROC SGPLOT and learn how to produce a heat map together with a table, outline select cells, and display multiple values within a cell.

 

Tech Support Points Out

 
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Estimating Nonlinear Combinations of Model Parameters

The NLEstimate macro enables you to estimate one or more linear or nonlinear combinations of parameters from any model for which you can save the model parameters and their variance-covariance matrix. Most modeling procedures that offer ESTIMATE, CONTRAST, or LSMEANS statements provide only for estimating or testing linear combinations of model parameters. However, common estimation problems often involve nonlinear combinations, particularly in generalized models with nonidentity link functions such as logistic and Poisson models. For example, in a logistic model you might want to estimate the difference in probabilities (means) between two groups. A linear contrast only enables you to estimate the difference in log odds from which an odds ratio can be estimated. Estimating the difference in means in any generalized linear model that doesn’t use an identity link requires estimating a nonlinear function of the model parameters. The NLEstimate macro makes estimating such nonlinear functions easy.

 

Talks and Tutorials

 
 

Upcoming Conferences

MWSUG - MidWest SAS Users Group
Oct. 9–11, 2016
Cincinnati, OH

SESUG - SouthEast SAS Users Group
Oct. 16–18, 2016
Bethesda, MD

SCSUG - South Central SAS Users Group
Nov. 6–8, 2016
San Antonio, TX

 

Resources

 

SAS® Statistics and Operations Research News

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