R help - Feb 2009 - formula formatting/grammar for regression

Hi all,

I am doing some basic regression analysis, and am getting a bit
confused on how to enter non-polynomial formulas to be used.

For example, consider that I want to find A and r such that the
formula y = A*exp(r*x) provides the the best fit to the line y=x on
the interval [0,50].

I can set:
xpts <- seq(0, 50, by=0.1)
ypts <- seq(0, 50, by=0.1)

I know I can find a fitted polynomial of a given degree using
lm(ypts ~ poly(xpts, degree=5, raw=TRUE))

But am confused on what the formula should be for trying to find a fit
to y = A*exp(r*x).

If anyone knows of a resource that describes the "grammar" behind
assembling these formulas, I would really appreciate being pointed in
that direction as I can't seem to find much beyond basic polynomials.

Thanks for the help!

Brigid Mooney <bkmooney <at> gmail.com> writes:
> I am doing some basic regression analysis, and am getting a bit
> confused on how to enter non-polynomial formulas to be used.
..
> But am confused on what the formula should be for trying to find a fit
> to y = A*exp(r*x).
If this example is just a placeholder for "more complex than poly",
you should check function nls which works for non-linear functions.

However, if you really want to solve this problem only, doing a 
log on you data and fitting a log of the above function with lm()
is the easiest way out. Results can be a bit different from the
nonlinear case depending on noise, because in one case weight
are log-weighted, in the other linearly.

Dieter