Contents
How to use MCMC for logistic regression in SAS?
SAS access to MCMC for logistic regression is provided through the bayes statement in proc genmod. There are several default priors available. The normal prior is the most flexible (in the software), allowing different prior means and variances for the regression parameters. The prior is specified through a separate data set.
Which is the best method for logistic regression?
Another option is to use Bayesian methods. Here we focus on Markov chain Monte Carlo (MCMC) approaches to Bayesian analysis. SAS access to MCMC for logistic regression is provided through the bayes statement in proc genmod. There are several default priors available.
What is NBI and NMC in logistic regression?
In the forgoing, nbi is the length of the burn-in and nmc is the total number of Monte Carlo iterations. The remaining options define the prior and request certain output.
When to use maximum likelihood based estimator in logistic regression?
It is used when the sample size is too small for a regular logistic regression (which uses the standard maximum-likelihood-based estimator) and/or when some of the cells formed by the outcome and categorical predictor variable have no observations. The estimates given by exact logistic regression do not depend on asymptotic results.
How to use linear regression in PROC MCMC?
This section illustrates some basic features of PROC MCMC by using a linear regression model. The model is as follows: for the observations . The following statements create a SAS data set with measurements of Height and Weight for a group of children:
How is the posterior distribution of PROC MCMC determined?
PROC MCMC automatically obtains samples from the desired posterior distribution, which is determined by the prior and likelihood you supply. The following statements fit this linear regression model with diffuse prior information:
When to calculate Jeffreys prior in PROC MCMC?
The ZEROMATRIX function call assigns all elements in matrix m the value zero. To avoid redundant calculation, it is best to perform these calculations as the last observation of the data set is processed—that is, when ind is 39. You calculate Jeffreys’ prior in the BEGINNODATA and ENDNODATA statements.