Thank you for your reply.
Even though I specify REML=TRUE in the code, the fit was not done with REML.
My last question also was why I could not get an ANOVA output for the fixed
effects
> anova(g)
Error in anova(g) : single argument anova for GLMMs not yet implemented
Y
On Fri, Jul 20, 2012 at 11:51 AM, Douglas Bates wrote:
> On Thu, Jul 19, 2012 at 9:20 PM, Yolande Tra
> wrote:
> >
> > Dear Douglas,
> >
> > I am sorry to bother you but this is very important. I posted the
> following question (in a slight different version) at r-sig-ME question
> list but it seems no one is able to answer it.
>
> But Ben answered it. When you specify family="poisson" you are
> fitting a generalized linear mixed model. The parameter estimates
> provided for such a model by lme4 are the maximum likelihood
> estimates, up to an approximation. The default approximation is the
> Laplace approximation.
>
>
> This data has quite complicated design. I did not find any example
> that is similar in the literature on lme4. According to the
> investigator this is a partial nested design. Counts were collected at
> different transects, different depths and different sites at different
> times. Time is continuous and assumed to be random, all the others are
> categorical fixed where transect is nested within depth which is
> nested within site. Definitely the three factors are nested within
> each other but based on the the attached files and the table below, it
> looks like this a repeated measurement design where time (dive_id) is
> nested within the three factor level combination. So far if I am
> wrong, please correct me. I believe the main effect is site (b) and
> level (a) is nested within depth(b) which in turn is nested within
> site(b). dive_id which represents also time is random.
> > I read some examples you gave. My output is different.
> > 1. The fit is done with Laplace approximation, not REML
> > 2. There is no residual random effect
> > 3. anova(g) did not give any output
> >
> > In this table the cell represents the number of times each combination
> was used to obtain the counts (based on the attached file).
> >
> >
> >
> >
> > Hopkins
> >
> > Lovers Point
> >
> > Point Pinos
> >
> > Total
> >
> > 5
> >
> > B
> >
> > 8
> >
> > 6
> >
> > 6
> >
> > 20
> >
> > M
> >
> > 8
> >
> > 6
> >
> > 6
> >
> > 20
> >
> > Total
> >
> > 16
> >
> > 12
> >
> > 12
> >
> > 40
> >
> > 10
> >
> > B
> >
> > 7
> >
> > 6
> >
> > 7
> >
> > 20
> >
> > M
> >
> > 7
> >
> > 6
> >
> > 7
> >
> > 20
> >
> > Total
> >
> > 14
> >
> > 12
> >
> > 14
> >
> > 40
> >
> > 15
> >
> > B
> >
> > 7
> >
> > 6
> >
> > 8
> >
> > 21
> >
> > M
> >
> > 7
> >
> > 6
> >
> > 8
> >
> > 21
> >
> > Total
> >
> > 14
> >
> > 12
> >
> > 16
> >
> > 42
> >
> > Total
> >
> > 44
> >
> > 36
> >
> > 42
> >
> > 122
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > d2 <- read.csv(file.path(dataDir,"aggregate_2008.csv"), as.is=T,stringsAsFactors
> = FALSE)
> > > a<-factor(d2$level)
> > > b<-factor(d2$site)
> > > c<-factor(d2$depth)
> > > g=lmer(total_count ~ b+(1|b:c)+(1|b:c:a)+(1|dive_id), d2,
> REML=TRUE,family = "poisson")
> > > summary(g)
> > Generalized linear mixed model fit by the Laplace approximation
> > Formula: total_count ~ b + (1 | b:c) + (1 | b:c:a) + (1 | dive_id)
> > Data: d2
> > AIC BIC logLik deviance
> > 1153 1169 -570.3 1141
> > Random effects:
> > Groups Name Variance Std.Dev.
> > dive_id (Intercept) 0.60707 0.77915
> > b:c:a (Intercept) 0.16273 0.40340
> > b:c (Intercept) 0.16273 0.40340
> > Number of obs: 122, groups: dive_id, 61; b:c:a, 9; b:c, 9
> >
> > Fixed effects:
> > Estimate Std. Error z value Pr(>|z|)
> > (Intercept) 1.98724 0.37388 5.315 1.07e-07 ***
> > bLovers Point 0.02358 0.53618 0.044 0.965
> > bPoint Pinos -0.43114 0.53273 -0.809 0.418
> > ---
> > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >
> > Correlation of Fixed Effects:
> > (Intr) bLvrsP
> > bLoversPont -0.697
> > bPointPinos -0.702 0.489
> >
> > > anova(g)
> > Error in anova(g) : single argument anova for GLMMs not yet implemented
> >
> > I really appreciate any of your insight as author of the package lme4.
> >
> > Yolande
>
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