[R-sig-ME] lmer predicted and fitted values differ

[ONKELINX, Thierry]

Dear Amy,

The functions that you used take only the fixed effects into account.
While fitted() takes both the fixed and the random effects into account.

What do you want to predict? The 'average' data (all random effect
levels = 0)? Data for existing levels of the random effects? Data for
new levels of the random effects?

Best regards,

Thierry

------------------------------------------------------------------------
----
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
  

> -----Oorspronkelijk bericht-----
> Van: r-sig-mixed-models-bounces at r-project.org 
> [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Amy Wade
> Verzonden: woensdag 24 november 2010 13:29
> Aan: r-sig-mixed-models at r-project.org
> Onderwerp: [R-sig-ME] lmer predicted and fitted values differ
> 
> Hello,
> 
>  
> 
> I am trying to predict response values for a model with new, 
> unmeasured parameter values. It is an lmer model constructed 
> using the lme4 package. I realise there is not a 
> 'predict.lmer()' function or similar for lmer models.
> Having searched on the internet I found 3 possible methods 
> for calculating predicted values given unmeasured values for 
> a given parameter in the model.
> 
> 
>  
> 
> This is my model:
> 
> data.m2<-data.frame(pods.bind,FUNGICIDE,N.TENTS,Day,HUMIDITY,P
> LOT,TREE)
> #where 'pods.bind' is cbind(Black.pods,healthy.pods)
> 
> m2<-lmer(pods.bind~FUNGICIDE+N.TENTS*Day+HUMIDITY*Day+(1|PLOT/
> TREE),family="
> quasibinomial")
> 
>  
> 
>  
> 
> Here are the possible methods:
> 
> #1
> 
> mm = model.matrix(terms(m2),data.m2)
> 
> data.m2$pods.bind = mm %*% fixef(m2)
> 
> predicted.1<-exp(data.m2$pods.bind)/(1+exp(data.m2$pods.bind))
> 
>  
> 
> #2
> 
> predict.lmerBin <- function(object, X){
> 
> if(missing(X))
> 
> X <- object at X
> 
> b <- fixef(object)
> 
> plogis(X %*% b)
> 
> }
> 
> predicted.2<-predict.lmerBin(m2)
> 
>  
> 
> #3
> 
> predicted.3<-exp(model.matrix(terms(m2),data.m2)%*%fixef(m2))
> 
>  
> 
>  
> 
> All three methods result in exactly the same predicted 
> values. However, when I plug in the real data I get different 
> values than I do for fitted(). This makes me doubt the 
> validity of my predicted values. Could anybody explain why 
> the predicted values, using these methods, differ from the 
> fitted values? 
> 
>  
> 
> Thanks for your help.
> 
>  
> 
> Amy
> 
>  
> 
> ..............................................................
> ..............
> ...............
> 
> Amy S. I. Wade
> 
> Centre for Agri-Environmental Research
> 
> University of Reading
> 
>  
> 
> 
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> 
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