I agree with Hans. You should probably plot your data to check whether
these random effects terms might improve model fit.

Also, If the data are zero-inflated inter alia check out the glmmADMB
package.

Cheers,

Neil


On Fri, Dec 13, 2013 at 6:03 PM, Hans Ekbrand <hans.ekbrand@gmail.com>wrote:

> On Thu, Dec 12, 2013 at 01:39:04PM -0500, Tim Howard wrote:
> > List members -
> > I am learning a lot, quickly, but still have a way to go. I would
> > greatly appreciate some help with model specification in glmer.
> > I can't find a good example that parallels what I've got.
> >
> > My dataset consists of spatially-balanced random samples of rare
> > plants within alpine summits. There were two sampling bouts (yr1 and yr2)
> > with yr2 collected 6 years after yr1. A new set of random plots were
> > collected at each bout (e.g. new estimate of the population, not repeated
> > measures). I would like to test the difference in plant density from yr1
> to yr2, overall.
> >
> > These are count data with many zeros, fitting a negative binomial
> distribution.
> >
> > This is what confuses me:  I ALSO have environmental information
> > that influences density, such as elevation, solar radiation, slope (and
> more)
> > I would like to include these variables in the model, but I am not
> > exactly sure how. Based on my reading, this is what I think I have:
> >
> > block random effects: summit
> > continuous random effects: elev, solrad, slope
> > fixed effect: time (=samp)
> > individual random effects to deal with overdisperson: plotID
> >
> > I have over 350 plots for each sample bout, spread among 17 summits.
> >
> > Given this I think my model is:
> >
> > mod <- glmer(count ~ samp + (1|summit) + (1|elev) + (1|solrad) +
> (1|slope) + (1|pltID),
> >        data=dat, family="poisson")
> >
> > My primary questions:
> > Is this the appropriate way to handle these environmental variables?
>
> >From my limited understanding of these issues, I'd say elev, solrad
> and slope should be fixed effects - the are universal in the sense
> that they are defined for every (imaginable) case.
>
> If they vary within each summit, you could - in addition to having
> them as fixed terms - also include them as random slope terms:
> (elev|summit) + (solrad|summit) + (slope|summit), if you want to
> explain (some of) the variance between summits.
>
> Since you do not have repeated measures of pltID, you can not include
> a random term for pltID - there is no variance within each value of
> pltID.
>
> I would model like this
>
> mod <- glmer(count ~ samp + (1|summit) + elev + solrad + slope, data=dat,
> family="poisson")
>
> And possibly try (but I don't think you have enough data for this)
>
> mod <- glmer(count ~ samp + (1|summit) + elev + solrad + slope +
> (elev|summit) + (solrad|summit) + (slope|summit), data=dat,
> family="poisson")
>
> _______________________________________________
> R-sig-mixed-models@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

	[[alternative HTML version deleted]]

