FAQ
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

--
Stephen Sefick
Research Scientist

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

## Search Discussions

•  at Sep 20, 2008 at 6:30 pm ⇧
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

--
Stephen Sefick
Research Scientist

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

--
Stephen Sefick
Research Scientist

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
•  at Sep 20, 2008 at 10:38 pm ⇧

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
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

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 ?ster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
•  at Sep 21, 2008 at 8:58 pm ⇧

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
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
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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