[R-sig-ME] modelling saturated random effects with glmm
jos matejus
matejus106 at googlemail.com
Mon Jul 27 16:19:14 CEST 2009
Dear all,
I was wondering whether anyone could enlighten me on the following.
Why is it I can fit a generalized linear mixed model (family = poisson
for example) with lmer where I have as many levels of my random effect
as data points whereas with a linear mixed effects model (gaussian
distributed errors) I get an error message. I understand that the
random effect variance is completely confounded with the residual
variance in the case of a linear mixed model, but why is this not so
with a generalized linear mixed model?
for example
data(ergoStool, package="nlme") # load data
ergoStool$rantest <- 1:36 #create a pseudo random effect to illustrate
library(lme4)
stool.lmm <- lmer(effort~Type+(1|rantest), data=ergoStool)
#Error: length(levels(dm$flist[[1]])) < length(Y) is not TRUE
stool.glmm <- lmer(effort~Type+(1|rantest) , family=poisson, data=ergoStool)
summary(stool.glmm)
Generalized linear mixed model fit by the Laplace approximation
#Formula: effort ~ Type + (1 | rantest)
Data: ergoStool
AIC BIC logLik deviance
19.47 27.39 -4.737 9.474
Random effects:
Groups Name Variance Std.Dev.
rantest (Intercept) 0 0
Number of obs: 36, groups: rantest, 36
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.14658 0.11396 18.836 <2e-16 ***
TypeT2 0.37469 0.14804 2.531 0.0114 *
TypeT3 0.23091 0.15263 1.513 0.1303
TypeT4 0.07503 0.15823 0.474 0.6354
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) TypeT2 TypeT3
TypeT2 -0.770
TypeT3 -0.747 0.575
TypeT4 -0.720 0.554 0.538
Many thanks in advance
Jos
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