R help - Jul 2013 - MGCV: overlay fitted (marginal) curves over a plot of the original data

Dear R users,

I?ve stumbled over a problem that can be easily seen from the R code below:

- When I use plot.gam() on a fitted model object, I get a nice and well-looking
smooth curve for all
terms in the model.

- However, when I use predict(model) for a given predictor, with values of all
other predictors set
to their means, the resulting curve doesn?t fit well at all.

Is there a way to "overlay" the curve produced by plot.gam() over a
plot of the original data?

Here?s some reproducible code with mgcv version 1.7-22 on R3.0.1 (Windows 7):

##

library(mgcv)
set.seed(2)
dat <- gamSim(1,n=400,dist="normal",scale=2)
b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
plot(b,select=1)

plot(y~x0,dat)
mydata=data.frame(x0=0:1,x1=mean(dat$x1),x2=mean(dat$x2),x3=mean(dat$x3))
lines(0:1,predict(b,mydata,type="response"))

##

Best wishes,
Christoph


-- 
PD Dr Christoph Scherber
Georg-August University Goettingen
Department of Crop Science
Agroecology
Grisebachstrasse 6
D-37077 Goettingen
Germany
phone 0049 (0)551 39 8807
fax 0049 (0)551 39 8806
http://www.gwdg.de/~cscherb1

Probably you didn't want to set x0=0:1? Here is some code, to do what you
want.
(The CI shape is not identical to the plot(b) version as the uncertainty
includes
the uncertainty in the other smooths and the intercept now.)

library(mgcv)
set.seed(2)
dat <- gamSim(1,n=400,dist="normal",scale=2)
b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
plot(b,select=1)

plot(y~x0,dat)
mydata=data.frame(x0=0:200/200,x1=mean(dat$x1),x2=mean(dat$x2),x3=mean(dat$x3))
pv <- predict(b,mydata,type="response",se=TRUE)
lines(mydata$x0,pv$fit)
lines(mydata$x0,pv$fit+2*pv$se.fit,lty=2)
lines(mydata$x0,pv$fit-2*pv$se.fit,lty=2)


  

On 16/07/13 09:52, Christoph Scherber wrote:
> Dear R users,
>
> I?ve stumbled over a problem that can be easily seen from the R code below:
>
> - When I use plot.gam() on a fitted model object, I get a nice and
well-looking smooth curve for all
> terms in the model.
>
> - However, when I use predict(model) for a given predictor, with values of
all other predictors set
> to their means, the resulting curve doesn?t fit well at all.
>
> Is there a way to "overlay" the curve produced by plot.gam() over
a plot of the original data?
>
> Here?s some reproducible code with mgcv version 1.7-22 on R3.0.1 (Windows
7):
>
> ##
>
> library(mgcv)
> set.seed(2)
> dat <- gamSim(1,n=400,dist="normal",scale=2)
> b <- gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
> plot(b,select=1)
>
> plot(y~x0,dat)
> mydata=data.frame(x0=0:1,x1=mean(dat$x1),x2=mean(dat$x2),x3=mean(dat$x3))
> lines(0:1,predict(b,mydata,type="response"))
>
> ##
>
> Best wishes,
> Christoph
>
>