I am fitting a two dimensional smoother in gam, say junk =
gam(y~s(x1,x2)), to a response variable y that is always positive and
pretty well behaved, both x1 and x2 are contained within [0,1].
I then create a new dataset for prediction with values of (x1,x2) within
the range of the original
Without seeing the data and results it's hard to say. mgcv::predict.gam is
already `safe' so that's not the issue. It's also pretty heavily tested, so a
problem with that function wouldn't be the first place I'd look. How `large
positive' are the predictions relative to the observed response?
Note: forwarded message attached.
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Subject: Re: [R] Prediction using GAM
To: Prof Brian Ripley [EMAIL PROTECTED]
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R has *two* gam() functions in contributed packages 'mgcv' and 'gam'.
Which is this?
Please see the posting guide and provide a reproducible example.
If this is package 'gam', prediction difficulties of this sort for the S
version are discussed in the White Book, MASS and elsewhere (but I
Recently I was using GAM and couldn't help noticing
the following incoherence in prediction:
data(gam.data)
data(gam.newdata)
gam.object - gam(y ~ s(x,6) + z, data=gam.data)
predict(gam.object)[1]
1
0.8017407
predict(gam.object,data.frame(x=gam.data$x[1],z=gam.data$z[1]))
