[R-sig-ME] lmer output shows laplace approximation not reml

[Douglas Bates]

On Thu, Jul 19, 2012 at 9:20 PM, Yolande Tra <yolande.tra at gmail.com> 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