[R] nls and R scoping rules

[joerg van den hoff]

I apologize for posting this in essence the second time (no light at the 
end of the tunnel yet..):

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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