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 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

--

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