It currently operates only with variance component models. The analysis produces a SAS data set containing a pseudo-random sample from the joint posterior density of the variance components and other parameters in the mixed model. This algorithm works by generating a pseudo-random proposal from a convenient base distribution, chosen to be as close as possible to the posterior. The proposal is then retained in the sample with probability proportional to the ratio of weights constructed by taking the ratio of the true posterior to the base density. If a proposal is not accepted, then a duplicate of the previous observation is added to the chain.
Valid values are as follows.
This data set must contain the TYPE and PARM1 -PARM n variables, where n is the largest number of parameters among each of the base densities. This is the default prior. This is useful for monitoring the progress of CPU-intensive runs. The default is 1000, but more accurate results are obtained with larger samples such as 10000. The default is 20.