[R] nls and R scoping rules

Prof Brian Ripley ripley at stats.ox.ac.uk
Thu Jun 10 10:55:11 CEST 2004

On Thu, 10 Jun 2004, joerg van den hoff wrote:

> 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 assume by `data' you mean not the `data' argument to nls but the 
variables referred to in your formula.

> 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).

S has frame 1 for this purpose.  The nearest equivalent I know in R is to 
attach an environment in position 2 and assign objects like 'fifu' there, 
which avoids your `undesirable effect'.

> 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).

I think that is simply wrong.  It is a _function_ in your non-linear model
definition that is not being found, not the variables (which you call
`data' above).  You do currently need to ensure that the functions in your
formulae are in scope when called from nlsModel.

Around R 1.2.x the notion was introduced that variables should be looked 
for in the environment of a formula.  Functions using model.frame got 
converted to do that, but nls did not.  I guess that the best way forward 
is to ensure that nls (and nlsModel) does search the environment of the 
formula for functions.

Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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