Improve the interpretation of your frequentist analysis output's strength of evidence by incorporating Bayes factor bounds using SAS.
In Chapter 3 of van Buuren’s Flexible Imputation of Missing Data a variety of methods for imputing univariate missing data are presented. This post will summarize these techniques and show how to implement them in SAS.
Last week we saw how to generate posterior samples using PROC MCMC for simple linear and logistic regression models. This week, I want to show how to sample regression lines from the data set returned by MCMC by plotting several sample regression linse on top of a scatter plot of the source data.
In this post I’ll show how to fit simple linear and logistic regression models using the MCMC procedure in SAS. Note that the point of this post is to show how the mathematical model is translated into PROC MCMC syntax and not to discuss the method itself.