is there a way to enforce that "nls" takes both, the data *and* the model definition from the parent environment? the following fragment shows the problem.
#======== cut here==========
wrapper <- function (choose=0)
{
x <- seq(0,2*pi,len=100)
y <- sin(1.5*x);
y <- rnorm(y,y,.1*max(y)) if (choose==0) {
rm(fifu,pos=1)
fifu <- function(w,x) {sin(w*x)}
}
else
assign('fifu',function(w,x) {sin(w*x)},.GlobalEnv)res <- nls(y ~ fifu(w,x),start=list(w=1)) res } #======== cut here==========
if called as "wrapper(1)" this runs fine because the fitting function "fifu" is assigned in the GlobalEnv.
if called as "wrapper(0)", "fifu" is defined only locally and "nls" (actually, "nlsModel", I think) does not know what I'm talking about.
I understand, the problem is that the scoping rules are such that "nls" does not resolve 'fifu' in the parent environment, but rather in the GlobalEnv. (this is different for the data, which *are* taken from the parent environment of the nls-call).
I tried some variants of using "eval" but without starting to modify "nls" itself there seems no way (up to now, anyway).
The solution to "assign" 'fifu' directly into the GlobalEnv does work, of course, but leads to the undesirable effect of accumulating objects in the workspace which are not needed there (and might overwrite existing ones).
in response to my first post, I got the hint that for "lm" the situation is different: it handles the above situation as desired (i.e. accepts local model definition). in so far one might even argue that this behaviour of "lm" and "nls" leads to an inconsistent behaviour of R in quite similar situations (e.g. replacing at some point a linar model by a nonlinear model in some large project is not achieved by simply replacing "lm" by "nls" somewhere deep down in the source code).
regards, joerg
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