[Rd] Understanding some code within nls()

[peter dalgaard]

Where do you see that?

I see two instances in nls.R of

setPars = function(newPars) {...

but they are both part of a list element assignment, not function definitions as such. I.e., they are used for exporting an internal function to the outside world. The function sets variables in the environment of its defining function (e.g. nlsModel()), hence the <<-  

AFAICT, the code you write would die in an infite recursion immediately.

- Peter



> On 24 Jun 2022, at 18:11 , J C Nash <profjcnash using gmail.com> wrote:
> 
> In working to try to improve the nls() function, a Google Summer of Code contributor and
> I have found our understanding of the setPars() function in nls() in base R to be
> inadequate.
> 
> setPars <- function(newPars) {
>   setPars(newPars)
>   resid <<- .swts * (lhs - (rhs <<- getRHS())) # envir = thisEnv {2 x}
>   dev   <<- sum(resid^2) # envir = thisEnv
>   if(length(gr <- attr(rhs, "gradient")) == 1L) gr <- c(gr)
>   QR <<- qr(.swts * gr) # envir = thisEnv
>   (QR$rank < min(dim(QR$qr))) # to catch the singular gradient matrix
> }
> 
> There is a similar but not identical function in the minpack.lm.
> 
> I'd be grateful if anyone who has ideas on how this function is working
> (in particular why the recursive call and then the "<<-" assignments are
> needed) would get in touch offline to profjcnash _at_ gmail.com. I suspect
> a list discussion is not useful without some preparatory work to narrow
> the queries.
> 
> My intention is to write up a 1-2 page explanation as a prelude to modifying
> code to introduce Marquardt stabilizations and bounds-constrained parameters,
> which nls() currently lacks. Also I have examples where minpack.lm seems
> to be failing to get bounds constraints correct. The nlsr package (Duncan
> Murdoch and myself) does get these correct, but focusses on solving the
> nonlinear least squares problem, and lacks many useful features for modeling
> that are found in nls(). However, nls() often fails due to "singular gradient"
> or else needs to use altered calls to apply special methods.
> 
> Assuming some success, I'll post links to the list.
> 
> John Nash
> 
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-- 
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
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