I had to deal with this issue yesterday, and I'm guessing many who use lme4
have this same issue because of the lack of a predict.lmer function. It
seems to me that one would need to plug the X %*% b output from the
predict.lmerBin function below into the logistic distribution to get the
actual predicted probabilities. In other words:
predict.lmerBin <- function(object, X){
if(missing(X))
X <- obj...@x
b <- fixef(object)
plogis(X %*% b)
}
The example below demonstrates the equivalence between plogis(X %*% b) and
predict.glm(..., type = "response",...) :
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- rep(c(1, 2), c(6, 6))
SF <- cbind(numdead, numalive=20-numdead)
budworm.lg <- glm(SF ~ sex*ldose, family=binomial)
summary(budworm.lg)
ld <- seq(0, 5, 0.1)
preds = predict(budworm.lg, newdata= data.frame(sex=1, ldose=ld),
type = "response")
X = as.matrix(data.frame(int=1,sex=1, ldose=ld, sex.ldose=1*ld))#, sex.ldose
= 2*ld))
b=coef(budworm.lg)
plogis(X%*%b) == preds
If anyone can see a problem with the above approach, please let me know as I
am using this for a paper.
-Solomon
Spencer Graves wrote:
>
> Checking 'help("lmer")' leads me also to 'help("lmer-class")', both
> of which are helpful. Unfortunately, I know of no general "predict"
> method that's available for an object of class 'lmer'. A preliminary
> function of that nature is as follows:
>
>
> predict.lmerBin <- function(object, X){
> # object has class "lmer"
> # X = model matrix with columns
> # matching obj...@x
> if(missing(X))
> X <- obj...@x
> #
> b <- fixef(object)
> X %*% b
> }
>
>
> ## To use this, you need to know how to use
> # 'preduct.glm'.
>
> # Example using data(Contraception), discussed in
> (mlmR <- vignette("MlmSoftRev")) # opens in Adobe Acrobat
> # To open a script file companion to vignette("MlmSoftRev")
> #edit(mlmR) # with Rgui
> #Stangle(mlmR$file) # with ESS
>
> fitBin <- lmer(use ~ urban+age+livch+(1|district),
> Contraception, binomial)
> predict.lmerBin(fitBin)
>
> Does this answer your question?
> Spencer Graves
> p.s. If your example had been simple and self-contained, it would have
> been to reply, because I could copy a few lines of R code from your
> email into R, tested a few ideas, and craft a reply in a very few
> minutes, if not seconds. Without that, crafting a sensible reply takes
> more time, partly because it's less clear what you need to know to move
> to the next step, and partly because after I guess what you are really
> asking, I must next hunt for a suitable example.
>
> T. Florian Jaeger wrote:
>> Hi,
>>
>> I am using lmer (from the package lme4) to predict a binary response
>> variable (REL) from a bunch of fixed effects and two random effects
>> (Speaker_ID and NPhead_lemma):
>>
>> fit <- lmer(REL ~
>> SPEAKER_GENDER +
>> log(SPEECHRATE) +
>> SQSPEECHRATE +
>> .....
>> + (1|Speaker_ID) + (1|NPhead_lemma),
>> family="binomial",
>> data=data.lmer,
>> method="Laplace", model=T, x=T)
>>
>> I would like to get classification accuracies for the derived model,
>> or even do some evaluation (cross-validation), but I cannot find a way
>> to get to the predicted values. As far as I can tell no predict method
>> has been implemented for lmer, right? I also was trying to figure out
>> whether lmer stores the predicted values somewhere (I read the
>> documentation and implementation summary,
>> /library/lme4/doc/Implementation.pdf).
>>
>> Many thanks for your help and my apologies if I overlooked something
>> really simple.
>>
>> Florian
>>
>> --
>> T. Florian Jaeger
>> Ph.D. student
>> Linguistics Department,
>> P: +1 (650) 725 2323
>> F: +1 (650) 723 5666
>> U: http://www.stanford.edu/~tiflo/
>>
>> ______________________________________________
>> r-h...@stat.math.ethz.ch mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide!
>> http://www.R-project.org/posting-guide.html
>
> ______________________________________________
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> PLEASE do read the posting guide!
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>
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