[R-sig-ME] Mixed effect logistic regression help
CJ Griffiths
Christine.Griffiths at bristol.ac.uk
Tue Mar 29 11:19:29 CEST 2011
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
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