Martin Maechler <[EMAIL PROTECTED]> writes:
> >>>>> "KKWa" == Ko-Kang Kevin Wang <[EMAIL PROTECTED]>
> >>>>> on Thu, 10 Jul 2003 23:00:00 +1200 (NZST) writes:
>
> KKWa> Try: ?lm
>
> no. see below
>
> KKWa> On 10 Jul 2003, Gorazd Brumen wrote:
>
> >> Date: 10 Jul 2003 12:54:46 +0200 From: Gorazd Brumen
> >> <[EMAIL PROTECTED]> To: [EMAIL PROTECTED]
> >> Subject: [R] Simple linear regression
> >>
> >> Dear all,
> >>
> >> My friend wants to fit a model of the type
> >>
> >> z = a x^n y^m + b,
> >>
> >> where x, y, z are data and a, b, n, m are unknown
> >> parameters.
> >>
> >> How can he transform this to fit in the linear regression
> >> framework? Any help would be appreciated.
>
> He can't. When all 4 a, b, n, m are parameters, this is a
> non-linear regression problem. --> Function nls()
>
> Now, effectively 2 of the 4 are linear, 2 are non linear;
> such a problem is denoted as `` partially linear least-squares ''
> In such a case it's quite important (for efficiency and
> inference reasons) to make use of this fact.
>
> ---> use nls(...., method = "plinear" , ....)
I think it should be 'algorithm = "plinear"'
The full call would be something like
nls(z ~ cbind(x^n*y^m, 1), data = mydata, start=c(n = 1.0, m = 2.0),
algorithm = "plinear")
Must the exponents n and m be positive? If so, I recommend using the
logarithm of the exponents as the parameters in the optimization
nls(z ~ cbind(x^exp(logn)*y^exp(logm), 1), data = mydata,
start=c(logn = 0., logm = log(2.0)), algorithm = "plinear")
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