[R-sig-ME] lmer model specification

[ONKELINX, Thierry]

Dear Gustavo,

1) id:sex makes only sense if for a given id, multiple levels of sex are possible. So an individual can be male at one occasion and female at the next occasion. That's probably not what you want.

I would rather add a temperature:sex interaction to the fixed effects.
lmer(y ~ f1 + f2 + f3 + temp*sex + (temp|id), data)

Fitting the sexes seperatly is not needed.

2) I would recode the data and add a couple ID (assuming each person in only part of one couple in your dataset).
lmer(y ~ f1 + f2 + f3 + temp * sex + (temp|couple) + (1|ID),  data)

The couple random intercept and slope takes care of the within couple correlation. The ID random effect takes care of variability within the couple.

Best regards,

Thierry

> -----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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> 
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