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.)
--
Stephen Sefick
Research Scientist
Southeastern Natural Sciences Academy
Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods. We are mammals, and have not exhausted the
annoying little problems of being mammals.
-K. Mullis
--
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
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