# Re: R-beta: nonlinear fitting

Jim Lindsey (jlindsey@luc.ac.be)
Thu, 12 Mar 1998 18:34:15 +0100 (MET)

```From: Jim Lindsey <jlindsey@luc.ac.be>
Message-Id: <9803121734.AA17337@alpha.luc.ac.be>
Subject: Re: R-beta: nonlinear fitting
To: wsimpson@uwinnipeg.ca (Bill Simpson)
Date: Thu, 12 Mar 1998 18:34:15 +0100 (MET)
In-Reply-To: <Pine.OSF.3.95.980312092714.1585B-100000@io.uwinnipeg.ca> from "Bill Simpson" at Mar 12, 98 09:36:15 am

>
> Thanks very much Douglas for the pointer to nlm.
> Maybe the "Notes on R" maintainer can add at least a mention of nlm in the
> section on nonlinear fitting?
>
> I never did nonlinear fitting in S-Plus before, so I have nothing to
> unlearn, but I was hoping someone could show me how to do a least squares
> fit with nlm.
>
> example:
> x<-c(1,2,3,4,5,6)
> y<-.3*x^-.6 +.2
> y<-y+rnorm(6,0,.01)
>
> Please show me how to fit the model
> y=a*x^b + c
> by least squares.  I would like parameter estimates plus the standard
> error for each param.

try
fn <- function(p) (y-p[1]*x^p[2]+p[3])^2
nlm(fn,p=c(0.3,-0.6,0.2))
Of course, you usually will not have such good initial estimates as
those I put in the p vector! There are a lot of further options to nlm
if you have convergence problems. See the help.
Jim

>
> (This example could replace the current nonlinear fitting section in
> "Notes on R")
>
> Thanks very much for any help!
>
> Bill Simpson
>
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```