R help - Dec 2009 - mgcv with discrete covariates, interaction term

I'm trying to build a model with an overall smooth function for all  
the data, plus an additional smooth function for *some* of the data.
If "ind" is my indicator variable (0 for some x, 1 for others), I  
imagined I could write it like this:

gam.obj <- gam(y ~ s(x) + ind*s(x))

but this does not work.

Does mgcv have a way of doing this? If not directly, is there a  
reasonable workaround?

On Mon, 2009-12-21 at 13:20 -0500, John Tillinghast
wrote:
> I'm trying to build a model with an overall smooth function for all  
> the data, plus an additional smooth function for *some* of the data.
> If "ind" is my indicator variable (0 for some x, 1 for others), I
> imagined I could write it like this:
> 
> gam.obj <- gam(y ~ s(x) + ind*s(x))
> 
> but this does not work.
> 
> Does mgcv have a way of doing this? If not directly, is there a  
> reasonable workaround?
> 
If I understand you correctly, yes. see ?s and argument 'by'. Something
like:

## some dummy data
dat <- data.frame(y=rnorm(100), x=c(rnorm(50,5), rnorm(50)),
                  ind = rep(c(1,0), each = 50))
## fit model
mod <- gam(y ~ s(x) + s(x, by = ind), data = dat)

This works when ind is a simple indicator, but if you have more levels
(as a factor), by = ind will fit a separate smooth to each level of ind.

HTH

G

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