Hi,
I'm trying to fit a not linear model with the nls function to some data.
So far this is the best fitting that i found.

download.file("http://dl.dropbox.com/u/29337496/data" , destfile="./data")
load("data")

plot (x06Veg,y06Pop)
nlmod=nls( y06Pop ~ B + A * log(x06Veg)   ,  start = list(A = 1, B = 1 ) )
points(x06Veg , predict(nlmod), col = 2)

co <- coef(nlmod)
f <- function(x, A, B) {B + A * log(x)}
curve(f(x,A=co["A"],B=co["B"]) , add=TRUE)

I would like to increase the curvature of the model and check if the
residual sum-of-squares decrease.

Should i just guess and change the main function (B + A * log(x06Veg))
with another one (e.g. B + A * sqrt(x06Veg)) and re-run or there is a
better way?

Thanks in Advance
Giuseppe

-- 
Giuseppe Amatulli
Web: www.spatial-ecology.net

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