[R] Statistical question re assessing fit of distribution functions.

[Timur Shtatland]

If one of the goals is the normality test, then there may be better
alternatives to the Kolmogorov-Smirnov test.
See an explanation on:
http://graphpad.com/FAQ/viewfaq.cfm?faq=959

The R implementation:
?shapiro.test

A casual search also turned this up:
http://tolstoy.newcastle.edu.au/R/help/04/09/3201.html
http://tolstoy.newcastle.edu.au/R/help/04/08/3121.html
http://www.karlin.mff.cuni.cz/~pawlas/2008/MAI061/dagost.R

Best,

Timur
--
Timur Shtatland, Ph.D.
Senior Bioinformatics Scientist
Agencourt Bioscience Corporation - A Beckman Coulter Company
500 Cummings Center, Suite 2450
Beverly, MA 01915
www.agencourt.com

On Mon, Sep 22, 2008 at 12:26 PM, Ted Byers <r.ted.byers at gmail.com> wrote:
>
> I am in a situation where I have to fit a distrution, such as cauchy or
> normal, to an empirical dataset.  Well and good, that is easy.
>
> But I wanted to assess just how good the fit is, using ks.test.
>
> I am concerned about the following note in the docs (about the example
> provided):  "Note that the distribution theory is not valid here as we have
> estimated the parameters of the normal distribution from the same sample"
>
> This implies I should not use ks.test(x,"pnorm",mean =1.187, sd =0.917),
> where the numbers shown are estimated from 'x'.  If this is so, how do I get
> a correct test?  I know I can not use different samples because of just how
> different the parameters are from one sample to the next, so using
> parameters estimated from the sample from week one to define the
> distribution function for ks.test will give a poor fit for the data from
> week two.  And the sample size is small enough that I would not have
> confidence in the parameters estimated from a portion of a samlpe to fit
> against the remainder of the sample.
>
> Thanks
>
> Ted
>
> --
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