Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x):
x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98, 1.03, 1.06) model1 <- function(x, xo, ym) ym * (x-xo)/x model2 <- function(x, xo, ym, k) ym * (x-xo)/(k+x-xo) model3 <- function(x, xo, ym) ym * (1-exp(-log(2)*(x-xo)/xo)) model4 <- function(x, xo, ym, k) ym * (1-exp(-log(2)*(x-xo)/k)) fit1 <- nls(y~model1(x, xo, ym), start=list(xo=0.5, ym=1)) fit2 <- nls(y~model2(x, xo, ym, k), start=list(xo=0.5, ym=1, k=1)) fit3 <- nls(y~model3(x, xo, ym), start=list(xo=0.5, ym=1)) fit4 <- nls(y~model4(x, xo, ym, k), start=list(xo=0.5, ym=1, k=1)) anova(fit1, fit2) anova(fit3, fit4) Models 1 and 2 are nested, as are models 3 and 4 (set k=xo), so they can be compared using anova. I am looking for a way to compare the non-nested models (ie models 1 and 3, and models 2 and 4), or better still, I would like to compare all 4 at once. A significance test would be ideal, but I am beginning to think that this may not make statistical sense. In that case, is there an appropriate measure of goodness of fit? I’d be very grateful if someone could put me on the right track. Thanks, Tom R version 2.14.2 (2012-02-29) Platform: x86_64-pc-mingw32/x64 (64-bit) locale: [1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 [3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C [5] LC_TIME=German_Germany.1252 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
