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]]
?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]] ______________________________________________ R-help at 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.
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]] ______________________________________________ R-help at 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.
The following function is designed to work with a logit link. It can
easily be generalized to work with any link. The SEs and CIs are
evaluated accounting for all sources of random variation. The plot
may not be much help unless there is just one explanatory variate.
`ciplot` <-
function(obj=glmm2, data=data.site, xcol=2, nam="litter"){
cilim <- function(obj, xcol){
b <- fixef(obj)
vcov <- summary(obj)@vcov
X <- unique(model.matrix(obj))
hat <- X%*%b
pval <- exp(hat)/(1+exp(hat)) # NB, designed for logit link
U <- chol(as.matrix(summary(obj)@vcov))
se <- sqrt(apply(X%*%t(U), 1, function(x)sum(x^2)))
list(hat=hat, se=se, x=X[,xcol])
}
limfo <- cilim(obj, xcol)
hat <- limfo$hat
se <- limfo$se
x <- limfo$x
upper <- hat+2*se
lower <- hat-2*se
ord <- order(x)
plot(x, hat, yaxt="n", type="l", xlab=nam,
ylab="")
rug(x)
lines(x[ord], lower[ord])
lines(x[ord], upper[ord])
ploc <- c(0.01, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9)
axis(2, at=log(ploc/(1-ploc)), labels=paste(ploc), las=2)
}
## Usage
glmm2 <- lmer(rcr ~ litter + (1 | Farm), family=binomial,
data=data.site)
ciplot(obj=glmm2)
John Maindonald email: john.maindonald@anu.edu.au
phone : +61 2 (6125)3473 fax : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
On 8 May 2008, at 8:00 PM, r-help-request@r-project.org
wrote:> From: "May, Roel" <Roel.May@nina.no>
> Date: 8 May 2008 12:23:15 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
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