[R] How to compare fit of linear and nonlinear models

[Joerg Trojan]

Dear statistics experts,

I'm looking for a way to compare the fit of the following three models:

LinModel <- lm(y ~ x)
LogModel <- nls(y ~ SSlogis(x, Asym, xmid, scal))
PotModel <- nls(y ~ a * x^n, start=list(a=1, n=1))

I am only interested in whether one of these models has substantial advances in
explaining the variance of y. So my original idea was simply to compare the
adjusted R squared values. This however seems to be problematic for nls models,
as I learned from an earlier thread on this issue (see
http://article.gmane.org/gmane.comp.lang.r.general/40727).

Then I thought about using AIC instead, but again this does not seem to be
trivial (see http://article.gmane.org/gmane.comp.lang.r.general/22438).

Do you have any suggestions on how I should proceed?


Best regards & thanks in advance,
Joerg