Thanks Jarrod. I'm afraid it was a case of copying code without
testing it and, as you say, it should have read
prior1ZA = list(R = list(V=diag(1), n=0.002), G = list(G1 =
list(V=diag(1), n=0.002))) without fix=2. I have tried the
trait:units model and I get significant traitza terms so I would
like to use the zapoisson rather than the overdispersed poisson.
I suppose what I am asking is can I now go on to use my more complex
model with rcov = ~ idh(trait):units?
Thanks for your help.
-----Original Message-----
From: Jarrod Hadfield [mailto:j.hadfield at ed.ac.uk]
Sent: 31 July 2012 18:53
To: Brickhill, Daisy
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] MCMCglmm zero-altered
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
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