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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