Dear list!

 
I am analyzing a large data set on
TREE hollow selection/non selection by a forest carnivore (Genetta genetta) in  South Portugal , based on daily telemetry study. The
response variable is binomial and I am using a GLMM to account for the effects
of individual (1 | fAnimal) carnivores (19 levels in the rainy season and 12
levels in dry season) across two seasons (rainy and dry).
 
I did
model selection for both seasons through the r package MuMIn!
 
And for
instance for rainy season I get the following:
 
Global model:
 
MRT <- glmer(TREE_01R ~
T_MAX_trR + PRECP_trR + poly(SH_CV_trR, 2, raw = T) + D_HUM_P_trR + D_RIP_tr2R
+ poly(FOR_T_PP_trR, 2, raw = T) + poly(FOR_S_PP_trR, 2, raw = T) + FOR_ED_tr2R
+ (1 | fAnimalR), family = binomial, data = RSITE)
summary(MRT)
 
Generalized linear mixed model fit
by the Laplace approximation 
Formula: TREE_01R ~ T_MAX_trR +
PRECP_trR + poly(SH_CV_trR, 2, raw = T) +     
D_HUM_P_trR + D_RIP_tr2R + poly(FOR_T_PP_trR, 2, raw = T) +      poly(FOR_S_PP_trR, 2, raw = T) + FOR_ED_tr2R
+ (1 | fAnimalR) 
   Data: RSITE 
   AIC   BIC logLik deviance
 502.3 576.9
-238.2    476.3
Random effects:
 Groups  
Name        Variance Std.Dev.
 fAnimalR (Intercept) 6.7989   2.6075  
Number of obs: 2291, groups:
fAnimalR, 19
 
Fixed effects:
                                Estimate Std.
Error z value Pr(>|z|)    
(Intercept)                       1.1124     4.0161  
0.277 0.781789    
T_MAX_trR                        -4.9968     1.1247 
-4.443 8.89e-06 ***
PRECP_trR                         0.8262     0.3283  
2.517 0.011851 *  
poly(SH_CV_trR, 2, raw = T)1    -12.5260     3.6186 
-3.462 0.000537 ***
poly(SH_CV_trR, 2, raw = T)2      2.8229    
1.5458   1.826 0.067827 .  
D_HUM_P_trR                      
0.4446     0.6504   0.684 0.494207    
D_RIP_tr2R                       
1.0808     0.1263   8.555 
< 2e-16 ***
poly(FOR_T_PP_trR, 2, raw =
T)1  53.8205     7.9867  
6.739 1.60e-11 ***
poly(FOR_T_PP_trR, 2, raw = T)2
-30.2637     4.5441  -6.660 2.74e-11 ***
poly(FOR_S_PP_trR, 2, raw = T)1
-22.3693     3.7820  -5.915 3.33e-09 ***
poly(FOR_S_PP_trR, 2, raw =
T)2  18.7899     3.3973  
5.531 3.19e-08 ***
FOR_ED_tr2R                      -1.6714     0.7988 
-2.092 0.036410 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’
1
 
 
ddMRT <- dredge(MRT, trace =
TRUE)
 
subset(ddMRT, delta < 2)
 
 
 
top.models <- get.models(ddMRT,
cumsum(weight) <= .95)
 
model.avg(top.models)
 
Component models: 
‘2345678’  ‘12345678’ ‘245678’   ‘1245678’ 
‘234568’  
 
Coefficients: 
                   (Intercept)                      D_RIP_tr2 
                     1.2306149                      1.0841739 
                    FOR_ED_tr2
poly(FOR_S_PP_tr, 2, raw = T)1 
                    -1.7958470                    -23.3256140 
poly(FOR_S_PP_tr, 2, raw = T)2
poly(FOR_T_PP_tr, 2, raw = T)1 
                    19.6395823                     52.8463876 
poly(FOR_T_PP_tr, 2, raw = T)2    poly(SH_CV_tr, 2, raw = T)1 
                   -29.5419047                    -11.9490352 
  
poly(SH_CV_tr, 2, raw = T)2                       PRECP_tr 
                     2.5727891                      0.8125258 
                      T_MAX_tr                     D_HUM_P_tr 
                    -5.0281052                      0.5707589
 
importance(model.avg(ddMRT,  rank 
=  AIC, subset = cumsum(weight)
<= .95))
 
 
D_RIP_tr2R poly(FOR_S_PP_trR, 2, raw = T) 
                     1.0000000                      1.0000000 
poly(FOR_T_PP_trR, 2, raw = T)    poly(SH_CV_trR, 2, raw = T) 
                     1.0000000                      1.0000000 
                     T_MAX_trR                      PRECP_trR 
                     1.0000000                      0.9412210 
                   FOR_ED_tr2R                    D_HUM_P_trR 
                     0.7836091                      0.3372627 
 
But in the final I need to get the
model average residuals for each season and also the residuals from random
effects (1 | fAnimal) to infer about model adequacy!
 
Do you know how to do it?
 
I´m reading the manual from the
MuMIn r package but I did not found any clue on that!
 
Thanks a lot!
 
Filipe Carvalho
 
Filipe Carvalho, MSc, PhD student.
Unidade de Biologia da Conservação (UBC) e
Centro de Investigação em Biodiversidade e 
Recursos Genéticos (CIBIO),
Universidade de Évora,
Casa Cordovil, 2º Andar, Rua Dr. 
Joaquim Henrique da Fonseca, 
7000-890 Évora, (PORTUGAL)
Telefone: + 351266759350


Filipe Carvalho, MSc, PhD student.
Conservation Biology Unit (UBC) and
Research Center in Biodiversity and 
Genetic Resources (CIBIO),
University of Évora,
Casa Cordovil, 2º Andar, Rua Dr. 
Joaquim Henrique da Fonseca, 
7000-890 Évora, (PORTUGAL)
Phone: + 351266759350
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