Dear useRs,
I am using the gamm function in the mgcv package to model a smooth relationship
between a covariate and my dependent variable, while allowing for
quantification of the subjectwise variability in the smooths. What I would
like to do is to make subjectwise predictions for plotting purposes which
account for the random smooth components of the fit.
An example. (sessionInfo() is at bottom of message) My model is analogous to
> out.gamm <- gamm( Y ~ Group + s(X, by=Group), random =
list(Subject=~1) )
Y and X are numeric, Group is an unordered factor with 5 levels, and Subject is
an unordered factor with ~70 levels
Now the output from gamm is a list with an lme component and a gam component.
If I make a data frame "newdat" like this:
> newdat
X Group Subject
5 g1 s1
5 g1 s2
5 g1 s3
6 g1 s1
6 g1 s2
6 g1 s3
I can get the fixed effects prediction of the smooth by
> predict(out.gamm$gam, newdata=newdat)
Which gives
1 1.1 1.2 2 2.1 2.2
3.573210 3.573210 3.573210 3.553694 3.553694 3.553694
But I note that the predictions are identical across different values of
Subject. So this accounts for only the fixed effects part of the model, and
not any random smooth effects.
If I try to extract predictions from the lme component:
> predict(out.gamm$lme, newdata=newdat)
I get the following error message:
Error in predict.lme(out.gamm$lme, newdata = newdat) :
Cannot evaluate groups for desired levels on "newdata"
So, is there a way to get subjectwise predictions which include the random
effect contributions of the smooths?
Thanks, John
---------
### session info follows
> sessionInfo()
R version 2.13.0 Patched (2011-06-20 r56188)
Platform: i386-pc-mingw32/i386 (32-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] grDevices datasets splines grid graphics utils stats
methods base
other attached packages:
[1] mgcv_1.7-6 gmodels_2.15.1 car_2.0-10 nnet_7.3-1
MASS_7.3-13 nlme_3.1-101
[7] rms_3.3-1 Hmisc_3.8-3 survival_2.36-9 lattice_0.19-26
loaded via a namespace (and not attached):
[1] cluster_1.14.0 gdata_2.8.2 gtools_2.6.2 Matrix_0.999375-50
tools_2.13.0
John Szumiloski, Ph.D.
Senior Biometrician
Biometrics Research
WP53B-120
Merck Research Laboratories
P.O. Box 0004
West Point, PA 19486-0004
USA
(215) 652-7346 (PH)
(215) 993-1835 (FAX)
john<dot>szumiloski<at>merck<dot>com
___________________________________________________
These opinions are my own and do not necessarily reflect that of
Merck & Co., Inc.
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