Bad scaling will waste a lot of everyone's time.
I put the data in a data frame mdat, then
library(nlmrt)
mdat<-read.csv("muzzamil.csv", header=T)
fmn <- nlxb(y~a * (x^b), data=mdat, start=c(a=1,b=1), trace=T)
fm <- nls(y~a * (x^b), data=mdat, start=c(a=1,b=1), trace=T)
fmn2 <- nlxb(y~a2 * ((x-1979)^b2), data=mdat, start=c(a2=1,b2=1), trace=T)
fm2 <- nls(y~a2 * ((x-1979)^b2), data=mdat, start=c(a2=1,b2=1), trace=T)
found a possible solution quickly with the last 2 forms. Without
adjusting the x data, you get a very ill-conditioned problem.
JN
> Message: 21
> Date: Wed, 14 Nov 2012 03:04:56 -0800 (PST)
> From: muzammil786 <m.shahbaz at sheffield.ac.uk>
> To: r-help at r-project.org
> Subject: Re: [R] problem in fitting model in NLS function
> Message-ID: <1352891096449-4649473.post at n4.nabble.com>
> Content-Type: text/plain; charset=us-ascii
>
> Dear David,
>
> I am having a similar problem on this data (given below). I have tried
> several starting values for a and b, but it is consistently giving me:
>
> *fm <- nls(y~f(x,a,b), data.frame(x,y), start=c(a=1,b=1))
> Error in numericDeriv(form[[3L]], names(ind), env) :
> Missing value or an infinity produced when evaluating the model*
>
> The function f(x,a,b) is defined here:
> *f <- function(x,a,b) {a * I(x^b)} *
>
> The data is given here:
>
> * x y
> 1 1980 1
> 2 1981 1
> 3 1982 1
> 4 1983 3
> 5 1984 3
> 6 1985 5
> 7 1986 8
> 8 1987 8
> 9 1988 9
> 10 1989 12
> 11 1990 15
> 12 1991 24
> 13 1992 33
> 14 1993 44
> 15 1994 62
> 16 1995 68
> 17 1996 81
> 18 1997 87
> 19 1998 102
> 20 1999 114
> 21 2000 123
> 22 2001 135
> 23 2002 144
> 24 2003 158
> 25 2004 172
> 26 2005 188
> 27 2006 197
> 28 2007 224
> 29 2008 234
> 30 2009 254
> 31 2010 278
> 32 2011 312
> 33 2012 317*
>
> I shall be grateful if you could guide me what's wrong here.
>
> Cheers.
> Muzammil
>
>
>
> --
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