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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