[R-sig-ME] comparing random effect variation between data sets

[John Morrongiello]

Thanks very much for this suggestion Ben. Makes good sense and I'll see how
it goes.

On Mon, Jun 29, 2015 at 1:21 PM, Ben Bolker <bbolker at gmail.com> wrote:

>   A couple of quick thoughts:
>
> * as a crude test, you could get the profile confidence intervals
> for the random-effect SDs in each model and compare them
> * a more formal test would put both species into the same model
> and allow for different variances.  This might work:
>
> library(lme4)
> library(gamm4)
> alldat <- rbind(data.frame(species="X",speciesX),
>                 data.frame(species="Y",speciesY))
>
> comb <- gamm4(distance ~ s(time,k=4,by=species),
>                   random=~(1|CODE)+(dummy(species,"Y")|CODE))
>
> You could test of comb against the model with just random=~(1|CODE),
> or look at the confidence intervals of the second RE term.
>
>
> On Thu, Jun 25, 2015 at 9:07 AM, John Morrongiello
> <jrmorrongiello at gmail.com> wrote:
> > Hi all
> >
> > I have movement data for two species of fish (say species X and species
> Y).
> > The response variable is 'distance from home' (distance) and multiple
> > measurements (5-17) are made for 20-30 individuals per species over a
> > period of 200 days. Distance is continuous and strictly positive so I am
> > using a gamma distribution with a log link. My collegues would like me to
> > fit seperate models for each species to explore temporal patterns in
> > movement (time). We believe there will be non-linear patterns in movement
> > through time so have I have fitted GAMMs
> >
> > I'm happy with the two models and their interpretation. I would, however,
> > like to compare the level of among-individual variation in movement
> between
> > species. The species-average trends are pretty similar but it looks like
> > there is more variance among individuals for species X than species Y.
> >
> > I was thinking of extracting the individual level random effects from the
> > two models and performing a variance test on these. My logic is that
> given
> > the response variables are the same and the model structures are the
> same,
> > I could make a meaningful comparison of random effects. The overall model
> > intercepts are different but I thought this is not an issue here as I'm
> > only interested in the variance of the random effects, not their average.
> >
> > Is this approach valid, or am I violating assumptions/ these data are not
> > directly comparable ? If not, what would be a potential way to perform
> this
> > comparision? Below is the code I'm thinking of using.
> >
> > Thanks very much for your time
> >
> > John
> >
> > #####GAMMs
> > X1<-gamm4(distance ~ s(time,k=4), random=~(1|CODE),data=speciesX,
> > family=Gamma(link=log))
> > Y1<-gamm4(distance ~ s(time,k=4), random=~(1|CODE),data=speciesY,
> > family=Gamma(link=log))
> >
> > ######extract random effects from the two models
> > X1ranef<-ranef(X1$mer)###extract random effects from model
> > X1ranef<-data.frame(matrix(unlist(X1ranef$'CODE'),ncol=1))##convert list
> of
> > random effects to data frame
> > names(X1ranef)<-c('CODE')
> >
> > Y1ranef<-ranef(X1$mer)###extract random effects from model
> > Y1ranef<-data.frame(matrix(unlist(Y1ranef$'CODE'),ncol=1))##convert list
> of
> > random effects to data frame
> > names(Y1ranef)<-c('CODE')
> >
> > ######perform variance test
> > var.test(X1ranef$CODE,Y1ranef$CODE)
> >
> >         [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

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