On 21/09/2008, at 10:38 AM, Peter Dalgaard wrote:
stephen sefick wrote:
I am not sure if I am exaggerating or not read title as hyperbola
On Sat, Sep 20, 2008 at 2:20 PM, stephen sefick
<[EMAIL PROTECTED]> wrote:
I have got a data set that is Gross Primary Productivity ~ Total
Suspended Solids it is a hyperbola just like:
plot(1/c(1:1000))
how do I model this relationship so that I can get all of the neat
things that lm gives residuals etc. etc. so that I can see if my
eyeball model stands up. Thanks for any help, pointers, or good
things to read.
Well, it depends on the exact model you want to fit and the error
characteristics.
There's a straightforward linear model in the transformed x:
lm(y ~ I(1/x))
but there are also transformed models like
lm(1/y ~ x)
or
lm(log(y) ~ log(x))
but of course, y, 1/y, and log(y) can't all be homoscedastic normal
variates. Going beyond the linearized models, you can use nls(), as in
nls(y~ a/(x-b), start=c(a=1,b=0))
(which is linear for 1/y, but assumes that y rather than 1/y has
constant variance.)
Nicely expressed. Succinct, clear, to the point, comprehensive. I
wish I'd said that!
(And that's not hyperbole. :-) )
So much more helpful than some postings I've seen recently to the
effect of ``Go away
and read a book on this topic.''
cheers,
Rolf
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