[R] Kolmogorov-Smirnov test

[Greg Snow]

A couple of things to consider:

The Kolmogorov-Smirnov test is designed for distributions on continuous variable, not discrete like the poisson.  That is why you are getting some of your warnings.

With a sample size over 10,000 you will have power to detect differences that are not practically meaningful.  You might as well use SnowsPenultimateNormalityTest (at least read the help page).

What are you trying to accomplish?  We may be able to give you a better approach.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of m.marcinmichal
> Sent: Wednesday, April 27, 2011 3:23 PM
> To: r-help at r-project.org
> Subject: [R] Kolmogorov-Smirnov test
> 
> Hi,
> I have a problem with Kolmogorov-Smirnov test fit. I try fit
> distribution to
> my data. Actualy I create two test:
> - # First Kolmogorov-Smirnov Tests fit
> - # Second Kolmogorov-Smirnov Tests fit
> see below. This two test return difrent result and i don't know which
> is
> properly. Which result is properly? The first test return lower D =
> 0.0234
> and lower p-value = 0.00304. The lower 'D' indicate that distribution
> function (empirical and teoretical) coincide but low p-value indicate
> that i
> can reject hypotezis H0. For another side this p-value is most higer
> than
> p-value from second test (2.2e-16). Which result, test is most
> propertly?
> 
> matr = rbind(c(1,2))
> layout(matr)
> 
> # length vectorSentence = 11999
> vectorSentence <- c(....)
> vectorLength <- length(vectorSentence)
> 
> # assume that we have a table(vectorSentence)
> #  1    2    3    4    5    6    7    8    9
> # 512 1878 2400 2572 1875 1206  721  520  315
> 
> # Poisson parameter
> param <- fitdistr(vectorSentence, "poisson")
> 
> # Expected density
> density.exp <- dpois(1:9, lambda=param[[1]][1])
> 
> # Expected frequ.
> frequ.exp <- dpois(1:9, lambda=param[[1]][1])*vectorLength
> 
> # Construct numeric vector of data values (y = vFrequ for Kolmogorov-
> Smirnov
> Tests)
> vFrequ <- c()
> for(i in 1:length(frequ.exp)) {
> 	vFrequ <- append(vFrequ, rep(i, times=frequ.exp[i]))
> }
> 
> # Check transformation plot(density.exp, ylim=c(0,0.20)) ==
> plot(table(vFrequ)/vectorLength, ylim=c(0,0.20))
> plot(table(vectorSentence)/vectorLength)
> plot(density.exp, ylim=c(0,0.20))
> par(new=TRUE)
> plot(table(vFrequ)/vectorLength, ylim=c(0,0.20))
> 
> # First Kolmogorov-Smirnov Tests fit
> ks.test(vectorSentence, vFrequ)
> 
> # Second Kolmogorov-Smirnov Tests fit
> ks.test(vectorSentence, "dpois", lambda=param[[1]][1])
> 
> # First Kolmogorov-Smirnov Tests fit return data
> 
> Two-sample Kolmogorov-Smirnov test
> 
> data:  vectorSentence and vFrequ
> D = 0.0234, p-value = 0.00304
> alternative hypothesis: two-sided
> 
> Warning message:
> In ks.test(vectorSentence, vFrequ) :
>   cannot compute correct p-values with ties
> 
> 
> # Second Kolmogorov-Smirnov Tests fit return data
> 
> One-sample Kolmogorov-Smirnov test
> 
> data:  vectorSentence
> D = 0.9832, p-value < 2.2e-16
> alternative hypothesis: two-sided
> 
> Warning message:
> In ks.test(vectorSentence, "dpois", lambda = param[[1]][1]) :
>   cannot compute correct p-values with ties
> 
> 
> 
> Best
> 
> Marcin M.
> 
> --
> View this message in context: http://r.789695.n4.nabble.com/Kolmogorov-
> Smirnov-test-tp3479506p3479506.html
> Sent from the R help mailing list archive at Nabble.com.
> 
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