From: Giovanni Bacaro <bacaro_at_unisi.it>

Date: Fri 26 May 2006 - 06:33:33 EST

Date: Fri 26 May 2006 - 06:33:33 EST

Dear list members,

First of all thank you for your helpful advices.
After your answeres to my firt mail I studied a lot (R-News n°5) and I
tried to perform my analysis:

First, to fit a GLM with a nested design I decided to use the function
"lmer" in package "lme4"

as suggested by Spencer Graves and Filippo Piro.
I remember you that my data were:

land use classes, 3 levels (fixed factor) = cla (R variable) plot number, 98 levels each with 4 replicates (random factor within "cla") = plotti (R variable)

number of species, totally 392 counts, response variable = sp (R variable)

Now, I startd my analysis as follow (after the creation of the data.frame "bacaro"):

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 0.145Received on Fri May 26 06:42:46 2006

> 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)? (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? 3) why the anova summary don't say if differences in classes are significance (or not significance)? 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@unisi.it ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

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