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. > > __________________________________________________ > > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
