Mixed effects model with binomial errors - problem
First of all I'm forwarding this mail to the R-SIG-mixed-models, which is more appropriate to your question. Remember that family = bionomial uses by default the logit link. Hence all parameters will be on the logit scale. So you will need to backtransform them for comparison. Then you'll see that the parameters are much closer to the averages. They still differ, but that is due to the difference in model. Your averages are essentially something like summary(model1<-glm(cbind(VisitsExpTree,TotalVisits-VisitsExpTree)~ treatment +(1|Individual), family=binomial, data=r))
library(boot) intercept <- 0.37228 treatmentb <- 0.03367 treatmentc <- -0.60606 treatmentd <- -0.25504 inv.logit(intercept)
[1] 0.5920098
inv.logit(intercept + treatmentb)
[1] 0.6001164
inv.logit(intercept + treatmentc)
[1] 0.4418197
inv.logit(intercept + treatmentd)
[1] 0.5292765 HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and quality assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx op inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-help-bounces op r-project.org [mailto:r-help-bounces op r-project.org] Namens RFTW Verzonden: vrijdag 19 september 2008 8:16 Aan: r-help op r-project.org Onderwerp: Re: [R] Mixed effects model with binomial errors - problem 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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