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Simon Wood's gamm() function in the mgcv package allows one
to fit smoothing terms through the gam() functionality and random
effects through the lme() functionality.
Keywords: predict, generalized additive mixed model.
- The output from the gam portion of the model is stored in mod$gam
and the lme portion in mod$lme (assuming mod is
the name of the gamm() model object.
- Note that predict(mod$gam) and predict(mod$lme)
give the same output, namely the predictions of interest.
- One use of this is that one can define one's own variance function.
Last modified 12/28/07.