To whom who can help me!

 

I want to run an lmer with a Poisson distribution because it is abundance
data (discrete data)named “ind_total”.

I have a nested structure which is “observations” within “localisation”
within “site”

The lme function would be: y~x1+x2+x3+random=1|site/localisation, data)

 

Actually, with lmer function I tried :
model_2_lmer<-lmer(ind_total~1+(1|site:localisation)+(1|site),data=donnees,m
ethod="Laplace",family=poisson(link = "log"),na.action=na.omit)

And a warning message appears saying:

 

Error in lmerFactorList(formula, mf, fltype) : 

  number of levels in grouping factor(s) ‘site’ is too large

Warning messages:

1: In site:localisation :

  l'expression numérique a 1278 éléments : seul le premier est utilisé
(numerical expression has 1278 elements : only first is used)

2: In site:localisation :

  l'expression numérique a 1278 éléments : seul le premier est utilisé
(numerical expression has 1278 elements : only first is used)

 

So I tried

model_3_lmer<-lmer(ind_total~1+(1|localisation)+(1|site),data=donnees,method
="Laplace",family=poisson(link = "log"),na.action=na.omit)

and it seems to work:

Generalized linear mixed model fit using Laplace 

Formula: ind_total ~ 1 + (1 | localisation) + (1 | site) 

   Data: donnees 

 Family: poisson(log link)

  AIC  BIC logLik deviance

 1032 1048 -513.1     1026

Random effects:

 Groups       Name        Variance   Std.Dev.  

 site         (Intercept) 2.0538e-01 4.5319e-01

 localisation (Intercept) 5.0000e-10 2.2361e-05

number of obs: 1278, groups: site, 40; localisation, 2

 

Estimated scale (compare to  1 )  0.7704331 

 

Fixed effects:

            Estimate Std. Error z value Pr(>|z|)  

(Intercept)  0.15660    0.07645   2.048   0.0405 *

 

Is my nested structure written right?

 

Then I tried to include all my covariates and a warning message appears
again:

model_4lmer<-lmer(ind_total~as.factor(annee)+temp+as.factor(pluie)+foret_tot
+superf_reelle+foret_tot*superf_reelle+as.factor(localisation)+as.factor(pic
)+axe_1+axe_2+as.factor(traitement)+as.factor(annee)*as.factor(traitement)+(
1|localisation)+(1|site),data=donnees,method="Laplace",family=poisson(link =
"log"),na.action=na.omit)

> summary(model_4lmer)

Erreur dans asMethod(object) : matrix is not symmetric [1,2]

 

I looked at my matrix and everything is ok… the only thing is I have 3 NAs
in my response variable but I specified “na.action=na.omit”…What am I doing
wrong!?!

Thank you

Claudie Desroches

------------

Claudie Desroches
étudiante au 2e cycle
Chaire de recherche du Canada en écologie spatiale et en écologie du paysage
Université de Sherbrooke: Département de Biologie
1-819-821-8000 poste 63468
HYPERLINK
"mailto:claudie.desroches@usherbrooke.ca"claudie.desroches@usherbrooke.ca

 



Checked by AVG. 

10:48
 

	[[alternative HTML version deleted]]


