 

Thank you very much for your help. After your advices, I tried to reorganize
my syntax and actually my model is quite good. As I wrote in my first
e-mail, my data are not-normally distributed (I used a box-cox power
transformation but the shapiro test was bad anyway ---to much zero for count
data).

For this reason I decided to move to the lmer function specifying poisson as
exponential function

I declared my function in this way, following your email:

 

lmermodel<-lmer(y ~ X4+X5 +(1| X3/X2/X1), family=poisson, data=xxx)

 

that became using my real variables as

 

lmerprova<-lmer(ricchezza.specifica~ESPOSIZION+NOME_SPECI+(1|UCP/UCS/concat_
ucs), family=poisson, data=ibl)

 

 

where, as in the first mail

 

First variable-->X1 (random)

Second var.       X2 (random nested in X1)

                          X3 (random nested in X2 and X1 consequently)

 

                          X4 (fixed and crossed with X1, X2, X3)

                          X5 (fixed and crossed with X1 and X2)

 

This is my output:

 

Generalized linear mixed model fit using Laplace 

Formula: ricchezza.specifica ~ ESPOSIZION + NOME_SPECI + (1 |
UCP/UCS/concat_ucs) 

   Data: ibl 

 Family: poisson(log link)

 AIC   BIC logLik deviance

 736 781.8   -358      716

Random effects:

 Groups               Name        Variance   Std.Dev.  

 concat_ucs:(UCS:UCP) (Intercept) 1.4701e-02 1.2125e-01

 UCS:UCP              (Intercept) 3.6115e-01 6.0096e-01

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

number of obs: 720, groups: concat_ucs:(UCS:UCP), 180; UCS:UCP, 86; UCP, 36

 

Estimated scale (compare to  1 )  0.783588 

 

Fixed effects:

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

(Intercept)                  0.478205   1.090559  0.4385    0.661

ESPOSIZION[T.E]              0.199585   1.091182  0.1829    0.855

ESPOSIZION[T.N]              0.274139   1.090669  0.2513    0.802

ESPOSIZION[T.S]             -0.103914   1.091137 -0.0952    0.924

ESPOSIZION[T.W]              0.176784   1.090766  0.1621    0.871

NOME_SPECI[T.Quercus rubra]  0.015468   0.169695  0.0912    0.927

NOME_SPECI[T.Tilia sp.]     -0.009484   0.175848 -0.0539    0.957

 

Correlation of Fixed Effects:

               (Intr) ESPOSIZION[T.E ESPOSIZION[T.N ESPOSIZION[T.S
ESPOSIZION[T.W NOME_r

ESPOSIZION[T.E -0.995


ESPOSIZION[T.N -0.995  0.998


ESPOSIZION[T.S -0.995  0.998          0.998


ESPOSIZION[T.W -0.995  0.998          0.998          0.998


NOME_SPECIr    -0.025 -0.020         -0.020         -0.020         -0.020


NOME_SPECIs    -0.027 -0.022         -0.022         -0.022         -0.022
0.280

 

Now, I'm trying to perform an anova analysis but, as expected reading the R
forum about lmer and p-value, no useful results were obtained.

 

> anova(lmerprova, test="Chsqr") 

Analysis of Variance Table

           Df  Sum Sq Mean Sq

ESPOSIZION  4 28.2627  7.0657

NOME_SPECI  2  0.0202  0.0101 

 

My questions are the following:

how can I compute the p-values for the anova?

Why random effects are not tested in the anova analysis?

And finally, how can I compute the residual variance and the amount of
variance explained from each variables?

Maybe I need to study a lot about this subject and mainly regarding the R
environment, however if you could suggest my some readings about, I'll
appreciate. Actually, I'm reading Fox 2002 and Bates 2005.

Thank you 

Giovanni

 


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