[R] Use generalised additive model to plot curve

[Gavin Simpson]

On Tue, 2010-12-14 at 22:36 +0000, e-letter wrote:
> Readers,
> 
> I have been reading 'the r book' by Crawley and think that the
> generalised additive model is appropriate for this problem. The
> package 'gam' was installed using the command (as root)
> 
> install.package("gam")
> ...
> library(gam)
> 
> > library(gam)
> Loading required package: splines
> Loading required package: akima
> > library(mgcv)
> This is mgcv 1.3-25

Why do you want to use both gam:::gam and mgcv:::gam at the same time?
Use one or the other package; not both.

> Attaching package: 'mgcv'
> 
> 
>         The following object(s) are masked from package:gam :
> 
>          gam,
>          gam.control,
>          gam.fit,
>          plot.gam,
>          predict.gam,
>          s,
>          summary.gam

These warnings/messages are because you loaded two packages that both
provide a gam() and other similarly named functions.

> > x<-c(30,50,80,90,100)
> > y<-c(160,180,250,450,300)
> > model<-gam(y~s(x))
> Error in smooth.construct.tp.smooth.spec(object, data, knots) :
>         A term has fewer unique covariate combinations than specified
> maximum degrees of freedom

You are trying to fit a thin-plate spline that is of greater complexity
than the available data. You need to reduce the complexity of the fitted
spline whilst fitting the model. For these data the maximum complexity
is 5:

> mod <- gam(y ~ s(x, k = 5))

HTH

G

> The objective is to plot y against x, finally to produce a graph with
> a smooth curve (and then remove the data points). What is my mistake
> please?
> 
> yours,
> 
> r251
> gnu/linux mandriva2008
> 
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