Depending on the assumptions you make about the error sturcture of your
model there are a couple of options:
1). Use transformations and lm (assumes lognormal multiplicitive
errors):
fit - lm( log(Y) ~ log(X) )
This finds log(bo) and b1
2). Use nonlinear least squares (assumes normal additive errors), look
at ?nls
3). Use more general algorithms, look at ?optim or maybe even the BRugs
package.
Hope this helps,
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
[EMAIL PROTECTED]
(801) 408-8111
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Milton
Cezar Ribeiro
Sent: Sunday, April 15, 2007 8:23 PM
To: R-help
Subject: [R] adjusting a power model in R
Dear R-gurus,
How can I fit a power model in R? I would like adjust Y =
b0*X^b1 or something like.
Kind regards,
Miltinho
Brazil.
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