exact values for p-values - more information.

Hi there,
Actually my aim was to compare anumber of extreme values (e.g. 39540)
with df1=1, df2=7025 via p-values.

Spencer mentions that

"However, I have also used numbers like
exp(-19775.52) to guestimate relative degrees of plausibility for
different alternatives."

Can someone point to me an article using this method?

Regards. Stephen.



----- Original Message -----
From: Spencer Graves <spencer.graves at pdf.com>
Date: Monday, July 11, 2005 7:39 pm
Subject: Re: [R] exact values for p-values - more information.

         I just checked:

pf(39540, 1, 7025, lower.tail=FALSE, log.p=TRUE)

[1] -Inf

         This is not correct.  With 7025 denominator degrees of
freedom, we
might use the chi-square approximation to the F distribution:

pchisq(39540, 1, lower.tail=FALSE, log.p=TRUE)

[1] -19775.52

         In sum, my best approximation to  pf(39540, 1, 7025,
lower.tail=FALSE, log.p=TRUE), given only a minute to work on
this, is
exp(pchisq(39540, 1, lower.tail=FALSE, log.p=TRUE)) = exp(-19775.52).

         I'm confident that many violations of assumptions would
likely be
more important than the differences between "p-value: < 2.2e-16"
and

 That doesn't mean they are right, only

the best
I can get with the available resources.

         spencer graves

Achim Zeileis wrote:

On Mon, 11 Jul 2005, S.O. Nyangoma wrote:

Hi there,
If I do an lm, I get p-vlues as

p-value: < 2.2e-16

This is obtained from F =39540 with df1 = 1, df2 = 7025.

Suppose am interested in exact value such as

p-value = 1.6e-16 (note = and not <)

How do I go about it?

You can always extract the `exact' p-value from the "summary.lm"

object or

you can compute it by hand via
  pf(39540, df1 = 1, df2 = 7025, lower.tail = FALSE)
For all practical purposes, the above means that the p-value is 0.
I guess you are on a 32-bit machine, then it also means that the

p-value

is smaller than the Machine epsilon
  .Machine$double.eps

So if you want to report the p-value somewhere, I think R's

output should

be more than precise enough. If you want to compute some other

values that

depend on such a p-value, then it is probably wiser to compute

on a log

scale, i.e. instead
  pf(70, df1 = 1, df2 = 7025, lower.tail = FALSE)
use
  pf(70, df1 = 1, df2 = 7025, lower.tail = FALSE, log.p = TRUE)

However, don't expect to be able to evaluate it at such extreme

values> such as 39540.

Z

project.org/posting-guide.html

--
Spencer Graves, PhD
Senior Development Engineer
PDF Solutions, Inc.
333 West San Carlos Street Suite 700
San Jose, CA 95110, USA

spencer.graves at pdf.com
www.pdf.com <http://www.pdf.com>
Tel:  408-938-4420
Fax: 408-280-7915