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 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
