All,
 
The archive email below mentions the lack of general predict methods in 
lme4.  I would like to plot the estimated growth curves for
treatment/placebo
groups (or the estimated patient growth curves that augment the former
w/ the patient estimated 
random effects).  
 
I obtained the plot manually by constructing the appropriate functions
from the fixed effects output and plotting using curve() (sample code
below).   
 
For those that have transitioned from lme, do you suggest sticking w/
such an approach 
(and possibly automating it w/ additional code, but this would be
dependent on the type 
of model that is estimated each time), or going back to lme where I
recall that this was relatively 
simple given the nesting mentioned by Douglas Bates below.
 
Thanks,
David
 
sample code:
## assume growth curve is quadratic, and coefficeints for placego group
are 10, 50, -20:
quad.fun.Placebo <- function(t) {
y = 100 + 50*t - 20*t^2
y}
## plot from t=0 to t=5
curve(quad.fun.Placebo, 0,5, xlab="Time", ylab="Dependent Variable",
col="red")
 
ps - why don't the methods show up below?
> library(lme4)
Loading required package: Matrix
Loading required package: lattice
> methods(class="lme4")
no methods were found
 
 
 
David Afshartous, PhD
University of Miami
School of Business
Rm KE-408
Coral Gables, FL 33124
 
 
From: Douglas Bates <bates_at_stat.wisc.edu
<mailto:bates_at_stat.wisc.edu?Subject=Re:%20%5BR%5D%20Predicted%20value
s%20in%20lmer%20modeling> > 
Date: Tue 28 Nov 2006 - 13:53:23 GMT


On 11/28/06, Fucikova, Eva <E.Fucikova@nioo.knaw.nl> wrote: 
> Dear All, 


> I am working with linear mixed-effects models using the lme4 package
in
<http://tolstoy.newcastle.edu.au/R/e2/help/06/11/6158.html#6180qlink1> 
> R. I created a model with the lmer function including some main
effects, 
> a two-way interaction and a random effect. Now I am searching for a
way 
> to save the predicted values for this model. 


> As far as I can see, there is no command in lme4 to save the predicted
<http://tolstoy.newcastle.edu.au/R/e2/help/06/11/6158.html#6180qlink2> 
> values (like the predict(model) function in e.g. glm). 


If you want the predictions at the observed values of the covariates you
can use 

 fitted(model) 

> This gives the following R output: Error in predict(lmer(model)) no
<http://tolstoy.newcastle.edu.au/R/e2/help/06/11/6158.html#6180qlink3> 
> applicable method for "predict" 


> I found the same question in the R forum archives, but no answer.
<http://tolstoy.newcastle.edu.au/R/e2/help/06/11/6158.html#6180qlink4> 


> Could anybody please give me an advice how to solve this problem?
<http://tolstoy.newcastle.edu.au/R/e2/help/06/11/6158.html#6180qlink5> 


I haven't written a general method for predict applied to an lmer object
because it is difficult to define what it should do. It is clear what
the predictions based on the fixed effects only should be and perhaps it
is clear what the standard errors of those predictions are (although
that would be a case where my favorite topic of the degrees of freedom
associated with a standard error would rear its ugly head again). 

It is trickier to define the predictions should be when you want to
incorporate the random effects. If you incorporate all the "levels" of
the random effects I think it is clear what the prediction should be.
Defining a standard error for that prediction could be difficult - I'm
not sure. However, I don't know what the answer should be if you only
incorporate some of the random effects. We could define that
unambiguously for lme models because the grouping factors were required
to be nested. Because lmer allows for fully crossed or partially crossed
grouping factors the concept of levels is lost. That is, there is no
strict hierarchy in the grouping factors and we can't levels to define
predictions. 

The bottom line is that I won't be able to write a predict method for
lmer objects until I can decide what it should do, what options should
be allowed and what the calling sequence should be. 

________________________________


R-help@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 and provide commented,
minimal, self-contained, reproducible code. Received on Wed Nov 29
18:44:37 2006 

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