The point is that you are checking the basis dimension used in the first
model, b, where the basis dimension for s(x2) was set to 6. All the
other model fits are about checking that first one. On checking the
residuals from model b you detect pattern with respect to x2, with an
estimated degrees of freedom of 9, which is bigger than the maximum
possible employed in model b. So model b is probably using too small a
basis dimension for s(x2).
best,
Simon
On 06/21/2012 02:07 AM, ywh123 wrote:
Hi,everyone,
I am studying the generalized additive model and employ the package 'mgcv'
developed by professor Wood.
However,I can not understand the example listed in check.in function.
For example,
library(mgcv)
set.seed(1)
dat<- gamSim(1,n=400,scale=2)
## fit a GAM with quite low `k'
b<-gam(y~s(x0,k=6)+s(x1,k=6)+s(x2,k=6)+s(x3,k=6),data=dat)
plot(b,pages=1,residuals=TRUE) ## hint of a problem in s(x2)
## the following suggests a problem with s(x2)
gam.check(b)
## Another approach (see below for more obvious method)....
## check for residual pattern, removeable by increasing `k'
## typically `k', below, chould be substantially larger than
## the original, `k' but certainly less than n/2.
## Note use of cheap "cs" shrinkage smoothers, and gamma=1.4
## to reduce chance of overfitting...
rsd<- residuals(b)
gam(rsd~s(x0,k=40,bs="cs"),gamma=1.4,data=dat) ## fine
gam(rsd~s(x1,k=40,bs="cs"),gamma=1.4,data=dat) ## fine
/gam(rsd~s(x2,k=40,bs="cs"),gamma=1.4,data=dat) ## `k' too low/
gam(rsd~s(x3,k=40,bs="cs"),gamma=1.4,data=dat) ## fine
why the model is not good for x2?
gam(rsd~s(x2,k=40,bs="cs"),gamma=1.4,data=dat) ## `k' too low
Family: gaussian
Link function: identity
Formula:
rsd ~ s(x2, k = 40, bs = "cs")
Estimated degrees of freedom:
9.0093 total = 10.00926
GCV score: 4.494652
For the results,we can see that the EDF is much less than K-1,so according
to
"If the effective degrees of freedom for a model term are estimated to be
much less than k-1 then this is unlikely to be very worthwhile",I think the
results are reasonable.
Why?
Thanks in advance
wanhai
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