[R] Kolmogorov-Smirnov test help

[Berton Gunter]

Re: Uwe's advice.

A quote from the late Ellis Ott (a U.S. quality control statistician of the
1970's and 80's):

"Look at your data -- and think!"

Cheers,

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA
 
"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box
 
 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Uwe Ligges
> Sent: Tuesday, November 22, 2005 2:28 AM
> To: lisadowson at gmail.com
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] Kolmogorov-Smirnov test help
> 
> Lisa Dowson wrote:
> 
> > Hi
> > 
> > I am conducting 2-sample Kolmogorov Smirnov tests for my 
> Masters project to
> > determine if two independant tree populations have the same 
> size-class
> > distribution or not. The trees have been placed into 
> size-class categories
> > based on their basal diameters. Once I started running the 
> stats on my data,
> > I got confused with the results. Just to show an example of 
> what I was
> > testing I ran stats comparing population1 to population 2. and then
> > comparing population 3 to population 2.
> >   Popn1 Popn2 Popn3  880 769 0  34 40 19  10 24 19  2 2 8  
> 2 2 36  0 0 0
> 
> If I interpret your data correctly, we can look at the step 
> functions by:
> 
>   plot.ecdf(x[,1], xlim=c(-10, 900), verticals=TRUE)
>   plot.ecdf(x[,2], xlim=c(-10, 900), col.hor="blue", col.vert="blue", 
> add=TRUE, verticals=TRUE)
>   plot.ecdf(x[,3], xlim=c(-10, 900), col.hor="red", col.vert="red", 
> add=TRUE, verticals=TRUE)
> 
> and see that there is no good reason why the test should 
> reject the Null 
>   for so few (6!) observations.
> 
> Note that you need at least 4 observations in each group to 
> be able to 
> reject anything at alpha=0.05 even for completely different 
> distributions:
> 
>   ks.test(1:3, 101:103)
> 
> 
> > Common sense tells me that P1 and P2 are similar and that 
> P3 and P2 are
> > dissimilar. However, for the P1 versus P2 test I am getting 
> p-value of 1
> > (saying they are strongly similar -what I expected) and for 
> the P3 versus P2
> > test, a p-value of 0.9307 (saying they are strongly 
> similar, but slightly
> > less similar than the other test- not what I expected at 
> all). However,
> > common sense tells me that P3 and P2 are not similar at all 
> and that I
> > should be expecting a far lower p-value for the test 
> comparing P3 and P2.
> 
> Sometimes common sense is dangerous. Sort your data, look again, look 
> into the plot, and rethink...
> 
> Uwe Ligges
> 
> 
> 
> 
> > Any help would be greatly appreciated
> > 
> > Thanks
> > 
> > Lisa
> > 
> > 	[[alternative HTML version deleted]]
> > 
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