[R] What is the most useful way to detect nonlinearity in logistic regression?

[Peter Dalgaard]

Peter Dalgaard <p.dalgaard at biostat.ku.dk> writes:


> Re. the smoothed residuals, you do need to be careful about the
> smoother. Some of the "robust" ones will do precisely the wrong thing
> in this context: You really are interested in the mean, not some
> trimmed mean (which can easily amount to throwing away all your
> cases...). Here's an idea:
> 
> 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.

Doh. You might also want to make sure that the residuals are of a type
that can be _expected_ to have mean zero. Apparently, the default
deviance residuals do not have that property, whereas response
residuals do. I did check that loess (as opposed to lowess!) does a
plain least-squares based fitting by default, but I didn't think to
check what kind of residuals I was looking at.

Serves me right for posting way beyond my bedtime...

Anyways, you're probably better off believing Frank and not me in
these matters.

-- 
   O__  ---- Peter Dalgaard             Blegdamsvej 3  
  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
 (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
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