[R] How to use lmer function and multicomp package?
Doran, Harold
HDoran at air.org
Sun Jun 4 13:38:35 CEST 2006
Comments below:
> mod1<-lmer(sp~cla+(1|cla:plotti), data=bacaro,
> family=poisson(link=log))
>
> > summary(mod1) #sunto del modello
>
> Generalized linear mixed model fit using PQL
> Formula: sp ~ cla + (1 | cla:plotti)
> Data: bacaro
> Family: poisson(log link)
>
> AIC BIC logLik deviance
> 451.2908 467.1759 -221.6454 443.2908
>
> Random effects:
> Groups Name Variance Std.Dev.
> cla:plotti (Intercept) 0.60496 0.77779 number of obs: 392,
> groups: cla:plotti, 98
>
> Estimated scale (compare to 1) 0.6709309
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) 2.06406 0.14606 14.1316 < 2.2e-16 ***
> cla2 -0.59173 0.17695 -3.3440 0.0008257 ***
> cla3 -0.74230 0.83244 -0.8917 0.3725454
> ---
> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>
> Correlation of Fixed Effects:
> (Intr) cla2
> cla2 -0.825
> cla3 -0.175 0.145
>
>
> > anova(mod1,test="Chsqr")
> Analysis of Variance Table
> Df Sum Sq Mean Sq
> cla 2 11.352 5.676
>
> Now, my questions are:
> 1) is the mod1 well specified? Have I said to R that "plotti"
> is my random factor and that "plotti" is nested inside "cla"
> ("cla" as grouping factor)?
Yes, you have plotti as a random factor nested in cla
(can be
> "lmer(sp~cla+(plotti|cla:plotti), data=bacaro,
> family=poisson(link=log)" an alternative solution?)
> 2) Why if I try "lmer(sp~cla+(1|cla:plotti),..." or
> "lmer(sp~cla+(1|plotti:cla),...." I obtain the same results?
Because this call is communative, so either way does not matter
> 3) why the anova summary don't say if differences in classes
> are significance (or not significance)?
See a recent post by Doug Bates at http://finzi.psych.upenn.edu/R/Rhelp02a/archive/76742.html
> 4) I'd like to perform a post-hoc test with the package
> "multicomp" but the lmer function give me a lmer object (and
> this kind of object is not read by the "multicomp" package).
> How could I perform my analysis in a different way?
>
> Thank you a lot for your help!
> Giovanni
>
>
>
> >I'd like to perform a glm analysis with a hierarchically
> nested design.
> In particular,
> >I have one fixed factor ("Land Use Classes") with three levels and a
> random factor ("quadrat") nested within Land Use Classes with
> different levels per classes (class artificial = 1 quadrat;
> class crops = 67 quadrats; and class seminatural = 30 quadrats).
> >I have four replicates per each quadrats (response variable = species
> richness per plot)
>
> >Here some question about:
> >1) could I analize these data using the class "artificial"
> (i.e. I have
> only 1 level)?
>
> >2) using R I'd like perfor a glm analysis considering my response
> variable (count of species) with a Poisson distribution. How
> can I develop my model considering the nested nature of my
> design? I'm sorry but I don't know the right package to use.
>
> >3) for the Anova analysis I'd like use a post-hoc comparison between
> pairwise classes. What is the right procedure to do this? Is
> this analysis performed in R?
>
>
> --
> Dr. Giovanni Bacaro
> Università degli Studi di Siena
> Dipartimento di Scienze Ambientali "G. Sarfatti"
> Via P.A. Mattioli 4 53100 Siena
> tel. 0577 235408
> email: bacaro at unisi.it
>
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