[R] GAMs in R : How to put the new data into the model?

[Gavin Simpson]

On Wed, 2011-09-28 at 02:10 -0700, pigpigmeow wrote:
> For example: 
> GAMs and after stepwise regression:

You probably don't want to be doing stepwise model/feature selection in
any regression model. Marra & Wood (2011, Computational Statistics and
Data Analysis 55; 2372-2387) show results that suggest adding an extra
penalty term during model fitting, that allows terms to be penalised out
of the model, performs well for feature selection in a series of GAM
exercises.

You can turn this on in mgcv::gam() via the `select` argument by setting
it to `TRUE`.

> cod<-gam(newCO~RH+s(solar,bs="cr")+windspeed+s(transport,bs="cr"),family=gaussian
> (link=log),groupD,methods=REML)
> 
> I used 10 year meterorology data (2000-2010) to form equation of
> concentration of carbon monoxide.
> NOW, I have 2011 meteorology data, I want to use the above GAMs to get the
> predict value of concentration of carbon monoxide.

So put your 2011 data into a data frame with (at the minimum) the column
names:

`RH`, `solar`, `windspeed`, `transport`

If that data frame were called, say, `dat2011` then you provide the
`predict()` method for "gam" objects with both the fitted model **and**
the new data (`dat2011`), e.g.:

pred <- predict(cod, newdata = dat2011)

See ?predict.gam for more details. You are probably going to need type =
"response" so you get values back on the scale of the response not the
the link function.

HTH

G

> How can I put the 2011 data into this gam and get the expected value of
> concentration of Carbon monoxide??
> 
> 
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