It is not clear from your post what changes per-group. If only the
starting values change (but the data and the model structure are the
same), then you can just store the starting values you want to use for
each group in a list, and then index into this list in your call to nls.
e.g., modifying an example in the help page for nls:
x <- 1:10
y <- 2*x + 3 # perfect fit
yeps <- y + rnorm(length(y), sd = 0.01) # added noise
startlist <- list(
list(a = 0.12345, b = 0.54321), ##group 1 start val
list(a = 0.12, b = 0.54) ## group 2 start val.
)
reslist <- list() ## filling this with results from different start val
for(i in 1:length(startlist)) {
reslist[[i]] <- nls(yeps ~ a + b*x, start = startlist[[i]],
trace = TRUE)
}
On Sun, 23 Sep 2007, Aleksi Lehtonen 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]]
>
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>
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