R help - Dec 2010 - Use generalised additive model to plot curve

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

Attaching package: 'mgcv'


        The following object(s) are masked from package:gam :

         gam,
         gam.control,
         gam.fit,
         plot.gam,
         predict.gam,
         s,
         summary.gam
> 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

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

Dear Lurker,

If all you art trying to do is to plot something, isn't all you need
something like the following?

x <- c( 30,  50,  80,  90, 100)
y <- c(160, 180, 250, 450, 300)
sp <- spline(x, y, n = 500)
plot(sp, type = "l", xlab = "x", ylab = "y", 
     las = 1, main = "A Spline Interpolation")
points(x, y, pch = 3, col = "red", lwd = 2) 

Bill Venables.

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of e-letter
Sent: Wednesday, 15 December 2010 8:37 AM
To: r-help at r-project.org
Subject: [R] Use generalised additive model to plot curve

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

Attaching package: 'mgcv'


        The following object(s) are masked from package:gam :

         gam,
         gam.control,
         gam.fit,
         plot.gam,
         predict.gam,
         s,
         summary.gam
> 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

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

______________________________________________
R-help at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

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
> 
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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