[R] Statistical question re assessing fit of distribution functions.
Ted Byers
r.ted.byers at gmail.com
Tue Sep 23 16:32:18 CEST 2008
Thanks Timur
While assessing whether or not the best option would be a normal
distribution (it won't be, the data in this case LOOKS more poisson, or if I
explude the first week of results, a negative exponential; and in my other
case, cauchy is more likely), I really need a test that can be applied
regardless of the distribution to see which distribution fits best. Using
log-likelihood, there doesn't seem to be much to choose between exponential
and poisson (the log-likelihhod for them being almost the same, regardless
of the sample even tough the parameters are very different from one sample
to the next - I don't understand why yet), and the others I have tried are
MUCH worse, but I'm not done yet.
Are you aware of functions that allow estimation of all the parameters of a
non-central distribution? I ask because a problem I'll be working on in a
few weeks will involve the kind of skew produced by a non-central
distribution (among others). I see some functions allow you to work with
skewed distributions (e.g. "[dpqr]stable the skewed stable distribution ")
but I have not yet found functions that alow one to estimate their
parameters from real data.
Thanks,
Ted
Timur Shtatland wrote:
>
> 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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>>
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>
> ______________________________________________
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>
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