﻿ Statistics and Operations Research Focus Area

# Welcome to Statistics and Operations Research

## Featured News

 SAS 9.4M5 Now Available SAS 9.4M5 was recently made available and includes the new SAS/STAT 14.3 and SAS/ETS 14.3 releases. SAS/STAT 14.3 includes a new procedure for causal mediation, along with cause-specific proportional hazards regression for competing-risks data in the PHREG procedure, as well as other new features. SAS/ETS 14.3 provides the new TMODEL procedure, which incorporates high-performance techniques and other new features that enhance the functionality of the MODEL procedure. Learn more about the highlights of all the updated analytical products.

May I Direct Your Attention to: The FASTQUAD Option in the GLIMMIX Procedure

One issue that often comes up with users is what to do when you have multilevel models in the GLIMMIX procedure where the number of random effects and/or nesting makes the model fitting very slow and sometimes impossible. The FASTQUAD option in the GLIMMIX procedure, available beginning in SAS/STAT 14.1, solves this problem in many cases by using an approximation method in the quadrature algorithm. The example in the documentation illustrates an application.

Heat Maps: Graphically Displaying Big Data and Small Tables

This paper shows you how to make a variety of heat maps by using PROC SGPLOT, the Graph Template Language, and SG annotation.

 Fitting and Assessing Proportional Odds Models to Ordinal Responses For an ordinal, multinomial response (such as low, medium, high), a set of cumulative response functions are simultaneously modeled. In an ordinal logistic model, the fully unrestricted model has a different parameter vector for each of the cumulative logit response functions. This is the nonproportional odds model. The much simpler proportional odds model restricts the parameters on each model effect to be equal so that only the intercepts vary across the parameter vectors. This is the model that PROC LOGISTIC fits by default. In between lie partial proportional odds models that restrict the parameters of only some effects to be equal. Two notes are available to help you assess whether the proportional odds restriction should apply to each model effect. Plots to assess the proportional odds assumption in an ordinal logistic model introduces code and a macro that uses the observed data to produce plots for assessing the assumption. Tests of the assumption are presented in The PROC LOGISTIC proportional odds test and fitting a partial proportional odds model.