Sampling methods for MCMCglmm using cengaussian family
Hmm, that makes sense, but I am not sure how to go about doing it. Okay, I am sure because I can see the code where it is done in C++, but I do not know an easy way and really loathe the idea of hacking source code, recompiling, finding an error, and cycling through that process until it works. I could be missing something because I am not the strongest at the theory underpinning these models. I did edit the R MCMCglmm function so I could look at all the output from the call to .C, but at least in the test case I created to try to mimic your example, there did not seem to be anything useful there. The process sounds like what the MICE package does, but in a bayesian framework. I know Jarrod is a busy fellow, but he usually periodically gets to emails here, and if he sees this, I am sure he would have a better answer/direction for you to take as there is still the real possibility I am missing something silly. Good luck, Josh
On Sun, Sep 30, 2012 at 11:50 AM, Robin Jeffries <rjeffries at ucla.edu> wrote:
Hi Joshua, Thank you for your response. I do have those Course Notes, but only skimmed the technical details b/c I don't have any RE. I'll look further into it. Thank you for looking into extracting the proposal variance, I don't have enough knowledge to look into or understand the guts of most programs, especially if they're in C. I know I can provide a proposal distribution, that's the entire point. I want to run this model for enough iterations such that the proposal distribution is "good" in that the acceptance rate is ~25% or so. Then I want to know what that proposal distribution is, so I can restart the model using this good proposal distribution with no burnin. This probably sounds strange, but this model is only a step in a larger cyclical algorithm (Sequential Regression Multiple Imputation (Raghunathan 2001)) that models multiple variables, one iteration at a time. Y1 is modeled, its results fed into the model for Y2, both those results are fed into Y3.... until the results from Y2-Yp are fed back into a model for Y1. So I need to draw 1 iteration at a time using a a constant proposal distribution that does not adapt. I was just hoping to avoid those re-calculations this time and have MCMCglmm just tell me what a good proposal variance was instead of having to figure it out myself :) FYI, The small priors were not intentional, essentially a typo. Thank you for pointing it out. Anyhow, thank you again for helping me figure out how I'm going to do what i need to do. I appreciate the time you spent on it. -Robin
Joshua Wiley Ph.D. Student, Health Psychology Programmer Analyst II, Statistical Consulting Group University of California, Los Angeles https://joshuawiley.com/