Re: [R] predict lmer

From: Kingsford Jones <kingsfordjones_at_gmail.com>
Date: Wed, 07 May 2008 13:59:50 -0700

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 <gunter.berton_at_gene.com> 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_at_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: r-help-bounces_at_r-project.org [mailto:r-help-bounces_at_r-project.org] On
> Behalf Of May, Roel
> Sent: Wednesday, May 07, 2008 7:23 AM
> To: r-help_at_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]]
>
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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. Received on Wed 07 May 2008 - 21:05:56 GMT

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