[R-sig-ME] Mixed effect logistic regression help

[CJ Griffiths]

Dear all,

I want to specify a mixed conditional logistic regression to model
microhabitat selection, but am unsure whether my dataframe and model are
correct. I want to compare parameters such as wind and temperature at the
location of the animal (1) to a random observation, where the animal was
absent (0). Each Response (1/0) is thus paired by the variable Micro. To
account for this pairing, I specified the random effect as 1|Micro.
However, I repeatedly sampled 11 animals (Ind). Random effect = 1|Ind

Is this the correct way to specify my random effects?
Have I labelled the “Ind” correctly? Originally I had each
Micro (pair of observations) labelled, i.e. where there was no animal I
still identified the “Ind” which that sample was collected
for.

My data looks like this:

'data.frame':   100 obs. of  7 variables:
$ Ind: Factor w/ 11 levels "a","b","c","d",..: 11 2 11 5 11 7 11 9 11 10 ...
 $ Response    : int  0 1 0 1 0 1 0 1 0 1 ...
 $ Micro   : int  1 1 7 7 8 8 9 9 10 10 ...
 $ Slope   : int  12 0 26 2 24 2 23 4 30 2 ...
 $ Wind    : int  4 4 3 3 2 1 2 2 3 2 ...
 $ Temp    : num  24.2 24.1 25.9 25.3 26.6 ...
 $ Cover   : int  0 60 2 25 5 70 20 90 30 95 ...

head(dataset)
   Ind Response Micro Slope Wind Temp Cover
1   na    0       1    12    4   24.2     0
2    b    1       1     0    4   24.1    60
3   na    0       7    26    3   25.9     2
4    e    1       7     2    3   25.3    25
5   na    0       8    24    2   26.6     5
6    g    1       8     2    1   29.2    70

MICRO<-as.factor(Micro)
y=cbind(Response,1-Response)

lmer(y~Temp+Wind+Slope+Cover+(1|MICRO)+(1|Ind),data=dataset,family=binomial,REML=0)

Generalized linear mixed model fit by the Laplace approximation
Formula: y ~ Temp + Wind + Slope + Cover + (1 | MICRO) + (1 | Ind)
   Data: dataset
   AIC   BIC logLik deviance
 17.39 35.63 -1.696    3.391
Random effects:
 Groups   Name        Variance   Std.Dev.
 MICRO    (Intercept) 5.6244e-12 2.3716e-06
 Ind      (Intercept) 7.8395e+03 8.8541e+01
Number of obs: 100, groups: MICRO, 50; Ind, 11

Fixed effects:
             Estimate Std. Error z value Pr(>|z|)
(Intercept)  14.49702  194.79843   0.074    0.941
Temp          0.03105    5.76866   0.005    0.996
Wind         -0.20947   15.36092  -0.014    0.989
Slope        -0.03506    2.19051  -0.016    0.987
Cover         0.02349    0.54182   0.043    0.965

Correlation of Fixed Effects:
      (Intr) Temp   Wind   Slope
Temp  -0.876
Wind  -0.506  0.498
Slope -0.143  0.071  0.086
Cover -0.112  0.060 -0.353 -0.173

According to the results from the above model, none of the variables have
a significant influence on the probability of finding an animal. Yet, when
I plot the data for say cover, there is a clear trend with animals being
more readily associated with high vegetation cover. Surely this should
result in a greater slope and a significant p-value.

The occurrence of this trend makes me suspect that the random effect
1|MICRO is not actually resulting in the comparison of parameters for 1 to
0 at a particular paired site. I would be grateful for confirmation and
advice on my model specification.

Many thanks,
Christine