Check out the subset= argument to nls, e.g. to regress Sepal.Length on Sepal.Width separately for each Species using the built in iris data set:
f <- function(s) nls(Sepal.Length ~ a*Sepal.Width, data = iris, start = c(a = 1), subset = iris$Species == s) sapply(levels(iris$Species), f, simplify = FALSE) On 9/23/07, Aleksi Lehtonen <[EMAIL PROTECTED]> wrote: > Dear Colleagues, > > I am trying to estimate several non-linear models simultaneously. I don't > want to use non-linear mixed model, but non-linear model with same form, but > it should be estimated separately according to variable group (I have lots > of groups that have lots of observations....). I would like to have unique > parameters for each group. > > e.g. something like this > > mod <- nls(y ~ a*x^b, start=c(a=1, b=1), group=group) > > but knowing that group option does not work. If someone has an idea (or has > done it already) how to implement this either using just nls statement or by > building a simple function in R, I would be very grateful for hints.... > > regards, Aleksi Lehtonen > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. > ______________________________________________ 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.
