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MCMCglmm zero-altered
2 messages · Brickhill, Daisy, Jarrod Hadfield
Hi, To use a ZAP model to test whether there is any zero inflation or deflation effects you want to hold the parameters constant across the Poisson and zero-altered part and compare them to a model in which they vary. For the random effects this comparison would be random=~colony versus something more complex (idh(trait):colony or us(trait):colony). For the fixed effects you want to compare ~1 versus ~trait and percent.grass2 versus trait:percent.grass2 etc. For the overdispersion term MCMCglmm will not allow you to have the same "residual" for both parts, but ~trait:units allows the "residuals" for both parts to have the same distribution (although information regarding its variance only comes from the Poisson part). I believe this still allows valid testing of whether there is any zero-alteration or not (but as always, could be wrong). In the trait:units model you do NOT want to fix the variance: in your second prior you had fix=2 despite estimating a single variance(V=diag(1)) so it was probably ignored anyway. I thought I had implemented MCMCglmm so it would generate an error if fix>nrow(V) - did you not get this? Cheers, Jarrod Quoting "Brickhill, Daisy" <r01db11 at abdn.ac.uk> on Tue, 31 Jul 2012 16:18:50 +0100:
Hi, I am currently modelling the effect of different habitat variables on the numbers of tipulid larvae found in soil cores using MCMCglmm. The data is slightly zero inflated so I am trying a zero-altered model (among others). I have used the following priors and model: prior1ZA = list(R = list(V=diag(2), n=0.002, fix=2), G = list(G1 = list(V=diag(2), n=0.002))) model1ZA <- MCMCglmm(no._tips ~trait*(percent.grass2 + mean.veg.ht + mean.soil.moisture + juldate + year),random = ~ idh(trait):colony,rcov = ~ idh(trait):units, family = "zapoisson", data = data, prior = prior1ZA, burnin = 3000, nitt = 1003000, thin=1000) However I have read in a previous post by the immensely helpful Jarrod Hadfield that "It is usual in zero-altered models to have the zero bit and the truncated poisson bit have the same over-dispersion. You do this by fitting the interaction rcov=~traits:units." I thought that ensuring the poisson and the zero process have the same over-dispersion would require priors and model of the form: prior1ZA = list(R = list(V=diag(1), n=0.002, fix=2), G = list(G1 = list(V=diag(1), n=0.002))) model1ZA <- MCMCglmm(no._tips ~trait*(percent.grass2 + mean.veg.ht + mean.soil.moisture + juldate + year),random = ~ trait:colony, rcov = ~ trait:units, family = "zapoisson", data = data, prior = prior1ZA, burnin = 3000, nitt = 1003000, thin=1000) But looking at other posts I am beginning to think I am missing something and that I *can* use my priors and model (with different variances for the zero and poisson parts of the model). Is this true? Can anyone tell me which of the two residual variance and random effect structures is most advisable? Many thanks, Daisy The University of Aberdeen is a charity registered in Scotland, No SC013683. [[alternative HTML version deleted]]
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