Dear all,

1. lets say that I want to look at the interaction between individual ID 
and temperature. The response variable is some behaviour, lets say 
aggression. Males and females might differ in the way they respond to 
temperature, so I want to take it into account. Should I run two 
different models, one for male and the other one for female, or should I 
include both in the same model:

mf1<-lmer(y ~ f1 + f2 + f3 + (temp|id:sex), data) - is this 
specification correct?

or

mfmales<-lmer(y ~ f1 + f2 + f3 + (temp|idmale), datamale)
mffemales<-lmer(y ~ f1 + f2 + f3 + (temp|idfemale), datafemale)


2. I also want to investigate if behaviour of the male affects the 
behaviour of the female. So I included the mate's behaviour as a fixed 
effect:

mfmales<-lmer(y ~ f1 + f2 + f3 + female's behaiour + (temp|idmale), 
datamale)

mffemales<-lmer(y ~ f1 + f2 + f3 + male's behaviour + (temp|idfemale), 
datafemale)

However, the variance and SD for the individual ID random effect is 
almost the same in both models (var=1.89 and SD=1.37), which tells me 
that there is something wrong. Both males and females were measured 
together and their behaviour is positive correlated (0.4). Should I 
include the mate's behaviour as a fixed effect?

Any help would be much appreciated.

Gustavo S. Betini



*Male:*

Linear mixed model fit by REML
Formula: pclm ~ datejcm + stagecm + mtempcm + windscm + pclf + (1 | 
site) +      (1 + mtempcm | id)
    Data: ndm
   AIC  BIC logLik deviance REMLdev
  2149 2197  -1064     2096    2127
Random effects:
  Groups   Name        Variance  Std.Dev. Corr
  id       (Intercept) 1.8947271 1.37649
           mtempcm     0.0074944 0.08657  0.428
  site     (Intercept) 0.1562428 0.39528
  Residual             1.4360044 1.19833
Number of obs: 589, groups: id, 83; site, 6

Fixed effects:
             Estimate Std. Error t value
(Intercept)  0.75545    0.23841   3.169
datejcm     -0.08297    0.02369  -3.503
stagecm      0.11100    0.02415   4.596
mtempcm      0.03910    0.02129   1.837
windscm     -0.08140    0.01944  -4.187
pclf         0.27352    0.03963   6.902




*Female:*

Linear mixed model fit by REML
Formula: pclf ~ datejcf + stagecf + mtempcf + windscf + pclm + (mtempcf 
|      id)
    Data: ndf
   AIC  BIC logLik deviance REMLdev
  2149 2193  -1065     2096    2129
Random effects:
  Groups   Name        Variance  Std.Dev. Corr
  id       (Intercept) 1.8914944 1.375316
           mtempcf     0.0055747 0.074664 0.640
  Residual             1.5423388 1.241909
Number of obs: 589, groups: id, 68

Fixed effects:
             Estimate Std. Error t value
(Intercept)  0.62346    0.17896   3.484
datejcf     -0.02476    0.01536  -1.612
stagecf      0.06675    0.01584   4.213
mtempcf      0.01501    0.02129   0.705
windscf      0.02313    0.02014   1.149
pclm         0.30024    0.03726   8.057



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