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Sane priors in a ZIP multilevel model

December 8, 2009

My recent tardiness from this blog shouldn’t be an indication of it’s abandonment. Instead it’s an indication of a hectic semester with a trying analysis.

I have been hard at work with a large data set with a dependent variable that has a Poisson distribution. Unfortunately, lots of subjects have zero bon this measure and have forced me into the arena of zero-inflated Poisson models because of poor fit of the Poisson models. The ZIP model in this case is essential a Poisson model + Binomial model for the zero-inflation component. Also due to the complexity of my analysis and the multiple levels involved, I can’t estimate this model with maximum likelihood but instead am relying on Markov Chain Monte Carlo and specifically the MCMCglmm package in R. The author of the package, Jarrod Hadfield, has been incredibly helpful and extremely patient with my questions. Unfortunately, my problem has even stumped him a little bit (the overall extremely poor fit of the predicted values from the Poisson model). Right now I am trying to figure out the most sane priors to expedite mixing and converging. At the moment MCMCglmm v. 1.13 only allows Inverse Wishart priors for the variance components and Normal priors for the means (which I’ve not specified but instead am using the default). However, Jarrod has informed that there will be a new version with a massive overhaul with more options for different priors. I’ve decided to use weak but informative Inverse Wishart priors while fixing the residual variance of the zero-inflation component because it can’t be estimated.

prior=list(R=list(V=diag(c(1,1e-10)), nu=0.001, fix=2),
G=list(G1=list(V=diag(2)*0.021e-10, nu=0.001)))

To run the ZIP model I have sent MCMCglmm the following code:
system.time(q1.zip <- MCMCglmm(y ~ trait + trait:time-1 + trait:I(time)^2,saveX=TRUE,saveZ=TRUE,random=~us(trait):id.f, nitt=200,burnin=50,rcov=~idh(trait):units,
data=dat1,verbose=TRUE,singular.ok=TRUE,pr=TRUE,
pl=TRUE,prior=prior,DIC=TRUE,family="zipoisson"))

However, MCMCglmm doesn’t like these prior as they are too weak:
Error in MCMCglmm(y ~ trait + trait:time - 1 + trait:I(time)^2, saveX = TRUE, : Mixed model equations singular: use a (stronger) prior Timing stopped at: 1485.42 44.79 1551.59

Back to the drawing board … I will update this when/if I can get this model to converge!

PS – This blog has subscribers! Thanks and I’ll try to update more often to make subscriptions actual worth it.

PSS – I will start working on a MS in Statistics in Fall 2010 and hope to finish it in Spring 2012 and then my PhD in Quantitative Methods in Education in Fall 2013. I think my MS will help provide me with a more thorough understanding of the statistical models I run as well as giving me a deeper understanding of Bayesian methods and MCMC. My plans are to continue to use this blog as a way to deliver Bayesian in a non-technical way to the applied researcher in the social sciences and maybe Ecology if someone can send some data!

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4 comments

  1. Any success yet?

    I am getting the same thing.

    Writing up my thesis now, but afterward I have screeds of eco data you could have a think about.

    Cheers,
    Alex.


    • Hi Alex,
      I have had success running the ZIP model in MCMCglmm with several different priors. I’d be happy to help out if I can. Also, I am preparing a presentation for a class at my University and I will post my presentation on my blog once it’s completed. (Should be up at the end of April).


      • Hi,

        Did you find the reasoning behind this error? I am also having trouble getting around it. Any prior I have tried so far is resulting in this error.

        Thanks,

        Sam


      • Hi Sam,
        I have not. I have not used MCMCglmm for a while and instead have been using primarily pscl to deal with these kinds of models. However, I am in the process of writing a package that allows multilevel data with hurdle, zip, and other mixed categorical models. It is uses a likelihood based approach. Once I get it really going I will link to on my blog.
        Chris



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