lmer predicted and fitted values differ

Dear Amy,

Here is a solution. It should work with nested random effects. Untested
with random slopes and crossed random effects.

Best regards,

Thierry

library(lme4)
cbpp$Obs <- factor(seq_len(nrow(cbpp)))
(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |
herd/Obs), family = binomial, data = cbpp))
str(gm1)

predict.lmerBin <- function(object, X, Zt){
	if(missing(X)){
		X <- object at X
	}
	b <- fixef(object)
	if(missing(Zt)){
		Zt <- as.matrix(object at Zt)
	}
	z <- unlist(ranef(object))
	plogis(X %*% b + t(Zt) %*% z)
}

all.equal(as.vector(predict.lmerBin(gm1)), fitted(gm1))

PS Replying to all should keep the thread intact.

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx at 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

lmer predicted and fitted values differ

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