-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-
bounces at r-project.org] Namens Gustavo Betini
Verzonden: vrijdag 19 augustus 2011 20:53
Aan: r-sig-mixed-models at r-project.org
Onderwerp: [R-sig-ME] lmer model specification
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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