Thank you to all.

I had actually found the summary and trotted that out.

Just had not gotten back to the list.

Thanks again!

Sincerely,
Erin



On Tue, Jul 1, 2014 at 5:46 PM, Bert Gunter <gunter.berton@gene.com> wrote:

> Beauty -- or obscurity -- is in the eyes of the beholder. But I leave
> your objections to stand without public response.If I can't stand the
> heat ... etc.
>
> However, I will say that my comment about the value of looking at the
> the RSS was meant to be helpful, because in my own consulting, I have
> seem many who believe that it is something that it is not. Deviance is
> the more useful statistical measure of model fit.
>
> Cheers,
> Bert
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
> (650) 467-7374
>
> "Data is not information. Information is not knowledge. And knowledge
> is certainly not wisdom."
> Clifford Stoll
>
>
>
>
> On Tue, Jul 1, 2014 at 2:29 PM, Rolf Turner <r.turner@auckland.ac.nz>
> wrote:
> >
> >
> > In direct contrast to what Bert says, I think this is a very reasonable
> (and
> > non-trivial) question.
> >
> > The problem results from Gurus structuring the functions that they write
> in
> > such a way that they are totally opaque to anyone but the
> ultra-cognoscenti.
> > What is gained by not having things set up in a straightforward manner
> that
> > is accessible to normal human beings is mysterious to me.
> >
> > If you do look at stats:::print.nls (and you have to start with that
> > "stats:::"; things just *have* to be hidden away so that normal human
> beings
> > can't see them!) you are likely to be no more enlightened than you were
> > previously, until you engage in a good long struggle.
> >
> > It turns out that what happens is that, in order to print the residual
> sum
> > of squares, print.nls() calls the function x$m$deviance (where "x" is the
> > object returned by nls()).  This function simply returns the object "dev"
> > which is stored in its environment.  Could one get more convoluted and
> > obscure if one tried?
> >
> > So, to get the residual sum of squares you could do:
> >
> >         rss <- x$m$deviance()
> > or
> >         rss <- get("dev",envir=environment(x$m$deviance))
> >
> > The actual residuals are hidden away as "resid" in the environment of the
> > function x$m$resid, so you could also get the residual sum of squares
> via:
> >
> >         rss <- sum(get("resid",envir=environment(x$m$resid))^2)
> > or
> >         rss <- sum(x$m$resid()^2)
> > or
> >         rss <- sum(resid(x)^2)
> >
> > the last of which applies the (hidden) nls method for the residuals()
> > function.  Happily, they all seem to give the same answer. :-)
> >
> > On 02/07/14 08:40, Bert Gunter wrote:
> >>
> >> 1. Why? What do you think it tells you?
> >
> >
> >         That's *her* business.
> >
> > (The number of parameters in a
> >>
> >> NONlinear model is probably not what you think it is).
> >>
> >> 2. ?deviance
> >
> >
> >         Not at all useful.
> >>
> >>
> >> 3. You've been posting all this time and still didn't try
> >> stats:::print.nls  ?? -- which is where you would find the answer.
> >
> >
> >         Chastising people for failing to see the invisible is not
> >         helpful.  And even when they manage to see the invisible, the
> >         result is still very obscure.
> >
> > cheers,
> >
> > Rolf
> >
> >>
> >> Cheers,
> >> Bert
> >>
> >>
> >>
> >> Bert Gunter
> >> Genentech Nonclinical Biostatistics
> >> (650) 467-7374
> >>
> >> "Data is not information. Information is not knowledge. And knowledge
> >> is certainly not wisdom."
> >> Clifford Stoll
> >>
> >>
> >>
> >>
> >> On Tue, Jul 1, 2014 at 1:27 PM, Erin Hodgess <erinm.hodgess@gmail.com>
> >> wrote:
> >>>
> >>> Hello R People:
> >>>
> >>> I'm having a forest/trees location problem with the output of nls.
> >>>
> >>> If I save the output to an object, and print the object, it shows,
> >>> amongst
> >>> other things, the residual sum of squares.  I would like to get that.
> >>>
> >>> However, when I look at names or str of the object, I can't find the
> >>> residual sum of squares.
> >>>
> >>> Any help would be much appreciated.
> >>> thanks,
> >>> Erin
> >
> >
>



-- 
Erin Hodgess
Associate Professor
Department of Mathematical and Statistics
University of Houston - Downtown
mailto: erinm.hodgess@gmail.com

	[[alternative HTML version deleted]]

