[R] MuMIn Problem getting adjusted Confidence intervals

[Marcos Lima]

Hello R users

I'm using MuMIn but for some reason I'm not getting the adjusted confidence
interval and uncoditional SE whe I use model.avg().

I took into consideration the steps provided by Grueber et al (2011)
Multimodel inference in ecology and evolution: challenges and solutions in
JEB.

I created a global model to see if malaria prevalence (binomial
distribution) is related to any life history traits of 14 different birds
species, while controling for Family and genus in a GLMM:

global.model.Para<-lmer(cbind(Parahaemoproteus,FailPh)~factor(SS)+factor(NT)+NH+W+IT+factor(MS)+(1|Family/Genus),family=binomial,data=malaria)

I than standardize the input variables using the function standardize form
the arm package:

stdz.model.Para<-standardize(global.model.Para,standardize.y=FALSE)

But I get this message:
Warning messages lost:
In is.na(thedata):
is.na() aplied to an object different from list or vector of type "Null"

summary(stdz.model.Para)

Generalized linear mixed model fit by the Laplace approximation 
Formula: cbind(Parahaemoproteus, FailPh) ~ factor(SS) + factor(NT) + z.NH +     
z.W + z.IT + factor(MS) + (1 | Family/Genus) 
   Data: malaria 
   AIC   BIC logLik deviance
 45.89 51.64 -13.95    27.89
Random effects:
 Groups       Name        Variance Std.Dev.
 Genus:Family (Intercept) 1.4262   1.1942  
 Family       (Intercept) 0.0000   0.0000  
Number of obs: 14, groups: Genus:Family, 12; Family, 5

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -4.6494     1.1791  -3.943 8.04e-05 ***
factor(SS)1   3.7793     2.0709   1.825    0.068 .  
factor(NT)1   1.8975     1.2793   1.483    0.138    
z.NH          0.4902     2.1099   0.232    0.816    
z.W          -1.6237     1.5957  -1.018    0.309    
z.IT         -0.7656     1.9598  -0.391    0.696    
factor(MS)1  -2.0603     1.3907  -1.481    0.138    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Correlation of Fixed Effects:
            (Intr) f(SS)1 f(NT)1 z.NH   z.W    z.IT  
factor(SS)1 -0.202                                   
factor(NT)1 -0.599  0.090                            
z.NH         0.058 -0.790 -0.178                     
z.W          0.232 -0.632  0.039  0.503              
z.IT         0.051  0.569  0.323 -0.851 -0.339       
factor(MS)1 -0.176 -0.632 -0.319  0.538  0.165 -0.567

I then proceed to use the dredge fucntion:
model.set.Para<-dredge(stdz.model.Para)
model.set.Para

Global model: glmer(formula = cbind(Parahaemoproteus, FailPh) ~ factor(SS) + 
    factor(NT) + z.NH + z.W + z.IT + factor(MS) + (1 | Family/Genus), 
    data = malaria, family = "binomial")
---
Model selection table 
   (Int)  fct(MS) fct(NT) fct(SS) z.I      z.N      z.W      k Dev.  AIC  
AICc  delta   weight
4  -5.231                 +                                  4 34.64 42.64
47.08  0.0000 0.290 
9  -4.750 +               +                                  5 30.00 40.00
47.50  0.4142 0.236 
.
.
.
Random terms: 1 | Family/Genus 

I then select the models with delta value up to 7:

top.models.Para<-get.models(model.set.Para,subset=delta<=7)
top.models

But when I do the model average I do not seem to be getting  the variance or
Uncoditional SE and I'm guessing that the Confidence interval are no
conditional either:

model.avg(top.models.Para,method="NA")

Model summary:
      Deviance  AICc Delta Weight
3        34.64 47.08  0.00   0.30
1+3      30.00 47.50  0.41   0.25
4+5      31.49 48.99  1.90   0.12
3+5      32.29 49.79  2.70   0.08
3+6      33.02 50.52  3.44   0.05
5        38.41 50.86  3.77   0.05
3+4      33.77 51.27  4.19   0.04
1+3+5    27.85 51.85  4.77   0.03
3+4+5    27.86 51.86  4.78   0.03
1+3+4    28.58 52.58  5.49   0.02
1+5      35.33 52.83  5.75   0.02
1+3+6    29.34 53.34  6.26   0.01
1+2+3    30.02 54.02  6.93   0.01

Variables:
         1          2          3          4          5          6 
factor(MS) factor(NT) factor(SS)       z.IT       z.NH        z.W 

Averaged model parameters:
            Coefficient    SE Lower CI Upper CI
(Intercept)       -4.75 1.410   -7.510  -1.9900
factor(MS)1       -1.54 0.809   -3.120   0.0471
factor(NT)1        2.28 1.310   -0.286   4.8500
factor(SS)1        3.30 0.968    1.400   5.2000
z.IT              -2.79 2.230   -7.160   1.5800
z.NH               2.28 1.660   -0.968   5.5300
z.W               -1.74 1.490   -4.650   1.1800
Confidence intervals are unadjusted 

Relative variable importance:
factor(SS) factor(MS)       z.NH       z.IT        z.W factor(NT) 
      0.82       0.33       0.32       0.20       0.07       0.01 

Does anyone know what I might be doing wrong? 

thanks for the help

Marcos

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