Message-ID: <20021206193251.GA8862@pcf004.jinr.ru>
Date: 2002-12-06T20:32:03Z
From: Timur Elzhov
Subject: Fitting 2D vs. 2D data with nls()
In-Reply-To: <D7A3CFD7825BD6119B880002A58F06C202F2C357@groexmb02.pfizer.com>
On Fri, Dec 06, 2002 at 01:57:28PM -0500, Warnes, Gregory R wrote:
> > Actually I want to fit y[,1] ~ x[,1] and y[,2] ~ x[,2]
> > *simulaneously*, with the same parameters set {p1, p2, p3}.
> Do you want to get separate estimates of p1, p2, and p3:
>
> one set for y[,1] ~ x[,1] and a separate set for y[,2] ~ x[,2],
>
> or do you want to get 3 common parameter values?
>
> For the latter, just do
>
> fit.result <- nls ( as.vector(y) ~ f(as.vector(x), p1, p2, p3),
> start = list(p1 = ... , p2 = .. , p3 = ..)
>
Yes! that is answer to question I posted! :-)
But.. I was actually wrong a bit, the situation is more
complicated:
I want *some* of the parameters to have common values,
and to be estimated seperately -- for the rest..
Thank you for your answer anyway, I couldn't guess
that simple solution even for common parameters.
WBR,
Timur