similar to: kolmogorov-smirnov for discrete ordinal scale data

I got a distribution function and a empirical distribution function. How do I make to Kolmogorov-Smirnov test in R. Lets call the empirical distribution function >Fn on [0,1] and the distribution function >F on [0,1] ks.test( ) thanks for the help -- View this message in context: http://www.nabble.com/Kolmogorov-Smirnov-test-tp23296096p23296096.html Sent

Although I saw this issue being discussed many times before, I still did not find the answer to: why does R can not calculate p-values for data with ties (i.e. - sample with two or more values the same)? Can anyone elaborate some details about how does R calculate the p- values for the Kolmogorov Smirnov test statistics? I can understand the theoretical problem that continuous distributions do

Hi r-users,   I would like to use Kolmogorov smirnov test but in my observed data(xobs) there are ties.  I got the warning message.  My question is can I do something about it?   ks.test(xobs, xsyn)           Two-sample Kolmogorov-Smirnov test data:  xobs and xsyn D = 0.0502, p-value = 0.924 alternative hypothesis: two-sided Warning message: In ks.test(xobs, xsyn) : cannot compute correct

I'm trying to use modified Kolmogorov-Smirnov test with a Normal which I don't know it's parameters. Somebody told me about the lilifor function in R, but just can't find it. Does anybody know how I can test with the modified Kolmogorov-Smirnov test? Porqu? usar una base de datos relacional cualquiera, si pod?s usar PostgreSQL?

Hello, I got a rather simple question: Can I find somewhere in R the significance values for a Kolmogorov distribution (I know the degrees of freedom and I have already the maximum deviation). ks.test is not really doing what I want. All I need is the values, like one can get the values for a chi-squared distribution by 'qchisq(0.05, 375)'. tnx, Kurt.

Hi - A little while ago I posted a question about the implementation of a two-dimensional analog of the Kolmogorov-Smirnov test in R[1][2]. As there isn't one, as far as I know, people might be interested in a very fast C++ implementation called MUAC which is available as a function and as a standalone program from http://www.acooke.org/jara/muac/index.html. Apparently the code is

Dear List, I am new to R and find it very powerful. I would like to compute the permutational p-value for paired data using Kolmogorov-Smirnov, but the built-in ks.test does not have this option, unlike the t.test which has a paired=TRUE flag. Has someone written a library or a routine that does this? Alternatively, if someone could show me how to do pair-wise permutations in R, then I can

Hello, I am curious if anyone has had any success with finding a R version of a k-sample Kolmogorov-Smirnov test. Most of the references that I have able to find on this are fairly old and I am wondering if this type of analysis has fallen out of favour. If so, how do people tend to compare distributions when they have more than two? Is it reasonable to pursue an adjusted p-value method. That is,

Hi, I have two sets of data, an observed data and generated data. The generated data is obtained from the model where the parameters is estimated from the observed data. So I'm not sure which to use either one-sample test ks.test(x+2, "pgamma", 3, 2) # two-sided, exact or two-sample test ks.test(x, x2, alternative="l") If I use the one-sample test I need to

I am hereby forwarding the data & method use to calculate the Kolmogorov-Smirnov goodness of fit test made manually by me in R launguage which deffers with the actual inbuilt formula as shown below. Further I have plot the graph in R. In that graph how to add trendline (i.e. straight line passing through maximum points in plot) to a Plot. R script is as follows please run this script to see

Hello, I would like to conduct a k-sample K-S test, but cannot find reference to its implementation in R. Does anyone have experience with this? Thanks, Mark [[alternative HTML version deleted]]

Dear R-users, I want to perform a One-Sample parametric bootstrapped Kolmogorov-Smirnov GoF test (note package "Matching" provides "ks.boot" which is a 2-sample non-parametric bootstrapped K-S version). So I wrote this code: ---[R Code] --- ks.test.bootnp <- function( x, dist, ..., alternative=c("two.sided", "less", "greater"), B = 1000 ) {

I've fixed this by adding 0.5/mn to q. The problem (at least in principle) with multiplying them all up is integer overflow. By the time 0.5/mn underflows to zero, missing one value in the distribution won't matter. -thomas On Fri, 18 Dec 2009, David John Allwright wrote: > Dear Thomas, Right, thank you. Yes, I haven't looked at the source code > (because I don't

Dear Thomas, Right, thank you. Yes, I haven't looked at the source code (because I don't know C) but something like what you mention could well cause the kind of problems I am seeing: a loop being exectued one too few or one too many times. And yes, I think those quantities should be multiplied up by m*n to all become integers so we escape rounding error problems. David.

Dear Thomas, Thank you, yes, that sounds good, and I take the point about integer overflow. Various questions: (a) Is there some way I can try out the routine with this modification? (I am on a Linux system where I am just a user - I cannot install new versions of software myself) ? (b) Is there a reference you can give me to a published paper where the method being used to compute the

What does 'with ties in' mean? with some identical elements (par ex., au moins une paire ex-equo) HTH ____________________ Ken Knoblauch Inserm U371, Cerveau et Vision Department of Cognitive Neurosciences 18 avenue du Doyen Lepine 69675 Bron cedex France tel: +33 (0)4 72 91 34 77 fax: +33 (0)4 72 91 34 61 portable: 06 84 10 64 10 http://www.lyon.inserm.fr/371/

Hi R-Users, I read some texts related to KS-tests. Most of those authors stated, that KS-Tests are not suitable for binned data, but some of them refer to 'other' authors who are claiming that KS-Tests are okay for binned data. I searched for sources and can't find examples which approve that it is okay to use KS-Tests for binned data - do you have any links to articles or

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

Hi all, given this example #start a<-c(0,70,50,100,70,650,1300,6900,1780,4930,1120,700,190,940, 760,100,300,36270,5610,249680,1760,4040,164890,17230,75140,1870,22380,5890,2430) length(a) b<-c(0,0,10,30,50,440,1000,140,70,90,60,60,20,90,180,30,90, 3220,490,20790,290,740,5350,940,3910,0,640,850,260) length(b) out<-ks.test(log10(a+1),log10(b+1)) # max distance D

Hi, Interpretation problem ! so what i did is by using the: >fit1 <- fitdist(vectNorm,"beta") Warning messages: 1: In dbeta(x, shape1, shape2, log) : NaNs produced 2: In dbeta(x, shape1, shape2, log) : NaNs produced 3: In dbeta(x, shape1, shape2, log) : NaNs produced 4: In dbeta(x, shape1, shape2, log) : NaNs produced 5: In dbeta(x, shape1, shape2, log) : NaNs produced 6: In