[R-sig-ME] fixed effects clarification/confirmation (ZIP model)

[Jarrod Hadfield]

Hi Ned, 

The estimated Poisson count for each of the four trt/phase combinations
can be obtained as: 

trtA and fphaseA  =  traity + at.level(trait, 1):trtA
trtA and fphaseB  =  traity + at.level(trait, 1):trtA +
at.level(trait,1):fphaseB
trtB and fphaseA  =  traity
trtB and fphaseB  =  traity + at.level(trait, 1):trtB:fphaseB

at.level(trait,1):trtB:fphaseB is therefore the difference between trtB
in fphaseA and trtB in fphaseB. 

MCMCglmm uses the standard call to model.matrix so standard R text-books
should give you an understanding of how the contrasts are formed. 

The ZIP model is essentially multi-trait with the first trait being the
Poisson part.  The fixed effect design matrix can be obtained as:

X<-maleF.zip at X[1:(dim(maleF.zip at X)[1]/2),]

which is often helpful.

The "standard" transformation of the fixed effects is complicated in
this analysis because you have a random intercept-slope model and so the
variance in the random effects is a function of the covariate. Also, is
it your intention to hold these effects constant over the Poisson part
and the zero-inflation part? Perhaps, using at.level(trait,1) within the
us() structure might be more appropriate?

Jarrod





On Thu, 2011-07-07 at 17:58 -0700, Ned Dochtermann wrote:
> Hi all,
> 
> I constructed a mixed-effect model about which I'm primarily interested in
> the fixed effects and I wanted to make sure that I was interpreting the
> output appropriately. Based on the MCMCglmm course notes I specified the
> following model:
> 
> maleF.zip<-MCMCglmm(attack.male~trait-1+at.level(trait,1):trt*at.level(trait
> ,1):fphase*at.level(trait,1):day.1+
> at.level(trait,1):temp.bar+at.level(trait,1):temp.dev,
> rcov = ~idh(trait):units,
> random=~us(1+tl.diff.f):subject,family="zipoisson",
> data=Compiled,prior=mf.prior,
> nitt=1080000,thin=480,burnin=120000,verbose=FALSE)
> 
> trt and fphase each have two levels (A&B for both)
> 
> The posterior mode estimates for the fixed effects from a shorter run are:
> posterior.mode(maleF.zip$Sol)
> traitattack.male       	traitzi_attack.male 	at.level(trait, 1):trtA 
> 11.74390983             	-2.52399057            	-0.23913921 
> at.level(trait, 1):fphaseB    	at.level(trait, 1):day.1
> at.level(trait, 1):temp.bar 
> -0.14206666                    	0.02169066                     	-0.43889554 
> at.level(trait, 1):temp.dev    	at.level(trait, 1):trtB:fphaseB
> at.level(trait, 1):trtB:day.1 
> -0.05774492                   	-0.24961100                    	-0.03413810 
> at.level(trait, 1):fphaseB:day.1		at.level(trait,
> 1):trtB:fphaseB:day.1 
> -0.02103302					0.11741711
> 
> My interpretation of the labeling of these effects is that
> traitzi_attack.male is the inflation latent factor. Of more interest to the
> questions I'm attempting to address, traitattack.male is the group mean for
> trtB with at.level(trait,1):trtA ( henceforth a.l():trtA ) being the
> difference between the group mean for trtA versus trtB. Picking an effect
> for example/clarification, I'm interpreting a.l():trtB:fphaseB as the
> difference from the global mean (traitattack.male) for trtB at fphaseB. Is
> this the correct interpretation? Also, since besides the inflation factor
> the effects are modeled as Poisson processes I've been assuming that the
> standard transformation of the fixed effects (averaged over random effects).
> 
> The layout is a bit different from other mixed model outputs (i.e. trtA for
> the first contrast rather than trtB being carried throughout) so I wanted to
> double check. I tried extracting the sparse fixed effects design matrix for
> clarification but it wasn't what I was expecting and didn't end up helping
> me.
> 
> Thanks for any help,
> Ned
> 
> --
> Ned Dochtermann
> Department of Biology
> University of Nevada, Reno
> 
> ned.dochtermann at gmail.com
> http://wolfweb.unr.edu/homepage/mpeacock/Ned.Dochtermann/
> http://www.researcherid.com/rid/A-7146-2010
> --
> 
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> 



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