Given that you don't have huge numbers of subjects you could fit the
model with `gam' rather than `gamm', using
out.gamm <- gam( Y ~ Group + s(X, by=Group) + s(Subject,bs="re"),
method="REML")
Then your predictions will differ by subject (see e.g. ?random.effects
for a bit more information on simple random effects in mgcv:gam).
A further trick allows you to choose whether to predict with the subject
effects at their predicted values, or zero.
Let dum be a vector of 1's...
out.gamm <- gam( Y ~ Group + s(X, by=Group) +
s(Subject,bs="re",by=dum), method="REML")
Predicting with dum set to 1 gives the predictions that you want.
Setting dum to 0 gives predictions with the prediction Subject effects
set to zero.
The reason that trying to predict with the gamm lme object is tricky
relates to how gamm works. It takes the GAMM specification, and then
sets up a corresponding `working mixed model' which is estimated using
lme. The working mixed model uses working variable names set within
gamm. If you try to predict using the working model lme object then
predict.lme looks for these internal working variable names, not the
variable names that you supplied....
Basically gamm treats all random effects as 'part of the noise' in the
model specification, and adjusts the variance estimates for the smooths
and fixed effects to reflect this. It isn't set up to predict easily at
different random effect grouping levels, in the way that lme is.
best,
Simon
On 24/06/11 15:59, Szumiloski, John wrote:
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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