One question that arises is: at what level is the prediction desired? Within a given ID:TRKPT2 level? Within a given ID level? At the marginal level (which Bert's code appears to produce). Also, there is the question: how confident can you be in your predictions. This thread discusses possible ways to get prediction intervals:
https://stat.ethz.ch/pipermail/r-sig-mixed-models/2008q2/thread.html#841 Finally, why assume a Poisson error distribution for a binary response? Kingsford Jones On Wed, May 7, 2008 at 10:13 AM, Bert Gunter <[EMAIL PROTECTED]> wrote: > Sorry, my reply below may be too terse. You'll need to also construct the > appropriate design matrix to which to apply the fixef() results to. > > If newDat is a data.frame containing **exactly the same named regressor and > response columns** as your original vdata dataframe, and if me.fit.of is > your fitted lmer object as you have defined it below, then > > model.matrix(terms(me.fit.of),newDat) %*% fixef(me.fit.of) > > gives your predictions. Note that while the response column in newDat is > obviously unnecessary for prediction (you can fill it with 0's,say), it is > nevertheless needed for model.matrix to work. This seems clumsy to me, so > there may well be better ways to do this, and **I would appreciate > suggestions for improvement.*** > > > Cheers, > Bert > > > > > -----Original Message----- > From: bgunter > Sent: Wednesday, May 07, 2008 9:53 AM > To: May, Roel; r-help@r-project.org > Subject: RE: [R] predict lmer > > ?fixef > > gets you the coefficient vector, from which you can make your predictions. > > -- Bert Gunter > Genentech > > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On > Behalf Of May, Roel > Sent: Wednesday, May 07, 2008 7:23 AM > To: r-help@r-project.org > Subject: [R] predict lmer > > > > Hi, > > I am using lmer to analyze habitat selection in wolverines using the > following model: > > (me.fit.of <- > lmer(USED~1+STEP+ALT+ALT2+relM+relM:ALT+(1|ID)+(1|ID:TRKPT2),data=vdata, > control=list(usePQL=TRUE),family=poisson,method="Laplace")) > > Here, the habitat selection is calaculated using a so-called discrete > choice model where each used location has a certain number of > alternatives which the animal could have chosen. These sets of locations > are captured using the TRKPT2 random grouping. However, these sets are > also clustered over the different individuals (ID). USED is my binary > dependent variable which is 1 for used locations and zero for unused > locations. The other are my predictors. > > I would like to predict the model fit at different values of the > predictors, but does anyone know whether it is possible to do this? I > have looked around at the R-sites and in help but it seems that there > doesn't exist a predict function for lmer??? > > I hope someone can help me with this; point me to the right functions or > tell me to just forget it.... > > Thanks in advance! > > Cheers Roel > > Roel May > Norwegian Institute for Nature Research > Tungasletta 2, NO-7089 Trondheim, Norway > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
