# [R] Mixed effects model with binomial errors - problem

RFTW l.temarvelde at nioo.knaw.nl
Fri Sep 19 08:15:32 CEST 2008

```anyone?

RFTW wrote:
>
> ok... the model now runs properly (say, without errors). Now about the
> result.
> These are the averages per treatments
>
> tapply(VecesArbolCo.VecesCo.C1,T2,mean)
>   a     b      c     d
> 0.49 0.56 0.45 0.58
>
>
> I run this very simple model
>
>> summary(model1<-lmer(cbind(VisitsExpTree,TotalVisits-VisitsExpTree)~
>> treatment +(1|Individual), family=binomial, data=r))
>
> Generalized linear mixed model fit by the Laplace approximation
> Formula: cbind(VisitsExpTree,TotalVisits-VisitsExpTree)~ treatment
> +(1|Individual)
>    Data: r
>    AIC   BIC logLik deviance
>  242.3 255.9 -116.2    232.3
> Random effects:
>  Groups    Name        Variance Std.Dev.
>  Individuo (Intercept) 0.14075  0.37517
> Number of obs: 112, groups: Individuo, 37
>
> Fixed effects:
>             Estimate Std. Error z value Pr(>|z|)
> (Intercept)          0.37228    0.19031  1.9562  0.05044 .
> treatmentb          0.03367    0.24520  0.1373  0.89079
> treatmentc         -0.60606    0.23330 -2.5978  0.00938 **
> treatmentd         -0.25504    0.22790 -1.1191  0.26311
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
>     (Intr) T2b    T2c
> T2b -0.675
> T2c -0.697  0.543
> T2d -0.720  0.544  0.581
>
>
> wouldnt we expect the intercept to be roughtly the mean of treatment a?
> and thus the estimate of treatmentb to be +0.07, c: -0.04 and d: +0.09
> roughly?
>
> Is this model just completely not estimating well, or are the estimates
> not the 'real values'.
>
> I tried to get teh predict function to give me the 4 predicted values
> based on the model, but i havent succeeded in doing so. maybe someone can
> help me on that one too (predict(model1,type="response") doesnt work)
>
> thnx
>

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