[R-sig-ME] Multi-response MCMCglmm (Gaussian and zapoisson)
susanne.lachmuth at botanik.uni-halle.de
Mon Feb 21 18:58:40 CET 2011
Dear MCMCglmm users,
I am currently struggling with the specification of a proper prior and model formula for a multi-response MCMCglmm with two of the three response variables being Gaussian and the third being za-poisson. The model includes several fixed effects and three nested random effects.
In general, I would prefer to fit a model with a fixed effect of trait and suppressed intercept for getting trait specific intercepts. Further, I wish to measure covariances between the response variables and consequently to specify completely parameterized covariance matrices.
My current model looks like this:
trait:(expl1 + expl2+ expl3+ expl4+ expl5….)+trait-1,
random=~us(trait):rand1+us(trait): rand1: rand2+us(trait): rand1: rand2: rand3,
However, for zero-altered models it is recommended in the MCMCglmm course notes to constrain over-dispersion to be the same for both processes (the zero-alteration and poisson process) by using a trait by unit interaction in the R-structure. Additionally, the intercept should not be suppressed for getting the differences between the zero-altered regression coefficients and the Poisson regression coefficients. This allows identification of zero-inflation or zero-deflation in response to the explanatory variables.
I therefore fit an additional model for the zapoisson response only, looking like this:
trait:(expl1 + expl2+ expl3+ expl4+ expl5….)+trait,
random=~trait:rand1+trait: rand1: rand2+ trait: rand1: rand2: rand3,
rcov=~trait:units, family="zapoisson",nitt=20000,burnin=1000,thin=10,data=dat, prior=prior2)
The results show that there is “significant” zero-inflation and deflation in response to some variables.
My main questions are:
Are the two priors specified correctly?
Does it make sense to include the zapoisson response (resp3) in model m1 and is the model formula (in particular the R-structure) appropriate?
An alternative might be to analyze resp1 and resp2 (both size variables of plants) with model m1 and fit an extra model (like m2) for resp 3 (reproduction) with the size parameters (i.e. responses of m1) as covariates. We are interested in a trade-off of growth and reproduction.
Any help would be greatly appreciated.
Department of Plant Ecolgy
University of Halle-Wittenberg
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