[R] What is the most useful way to detect nonlinearity in lo

[(Ted Harding)]

On 05-Dec-04 Peter Dalgaard wrote:
> (Ted Harding) <Ted.Harding at nessie.mcc.ac.uk> writes:
> 
>> >> x <- runif(500)
>> >> y <- rbinom(500,size=1,p=plogis(x))
>> >> xx <- predict(loess(resid(glm(y~x,binomial))~x),se=T)
>> >> matplot(x,cbind(xx$fit, 2*xx$se.fit, -2*xx$se.fit),pch=20)
>> >> 
>> >> Not sure my money isn't still on the splines, though.
> .....
>> > Serves me right for posting way beyond my bedtime...
>> 
>> Hi Peter,
>> 
>> Yes, the above is certainly misleading (try it with 2000 instead
>> of 500)! But what would you suggest instead?
> 
> (I did and this little computer came tumbling down...). 

So did mine -- but at 5000 (which is the value I first tried):
lots of disk grinding and then it went "prprprprp" and wrote
words to the effect "Calloc cannot allocate (18790050 times 4)"
i.e. it needed 72MB, which bankrupted my 192MB baby.

2000 was OK, however, but I had plenty of time for a meal etc.
before it finished.

Which brings up that predict(loess(....)) seems to be very
memory-hungry.

> Basically, I'd reconsider the type= option to residual.glm. As I said,
> at least type="response" should have the right mean. Ideally, you'd
> want to take advantage of the fact that the variance of the residuals
> is known too, rather than have the smoother estimate it. The more I
> think, the more I like the splines...

I'll have a go at your suggestions (if I can get the syntax right ... )

Thanks,
Ted.


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Date: 06-Dec-04                                       Time: 00:13:53
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