[R-sig-ME] Sampling methods for MCMCglmm using cengaussian family

Joshua Wiley jwiley.psych at gmail.com
Sun Sep 30 21:04:25 CEST 2012

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,


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

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