similar to: Two sample Kolmogrov Smirnov mutivariate test

I simulate a uniform data and then transformed into Burr(1,3,1) data, which is of pdf: f(x)=[3*(x^2)] / [(1+x^3)^2], x>0 How can I perform a goodness-of-fit test (k-s, anderson-darling,chisq,cramer-von mises,...) on it (should highly accept) to get test-statistics & p-values? Thanks! Sincerely, Shelton Jin -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Dear sir, I am using R in windows. I want to extend R Memory size. I use the following command, but unfortunately it doesn't work. -- vsize=15M --nsize=1000K Your help is appreciated. Thanks, Esmail Amiri. __________________________________________________ Do You Yahoo!? Get email at your own domain with Yahoo! Mail. http://personal.mail.yahoo.com/

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

Dear R users, I have seven regression lines I´d like to compare, in order to find out if these are significatively different. The main problem is that these are curves, non normal, non homogeneous data, I´ve tried to linearize them but it has not worked. So I´d like to know if you know any command or source in R which explains how to perform this kind of comparison. Thanks in advance for your

Dear R helpers: I have another question regarding "R" command. It is also measuring Credit Scoring. I need to measure the classifying method for customer's credit score. (ex. regression line by Fisher) If credit card company already classify their customers credit : Good customers / Bad customers [by using regression line; ex) inside of line: Good/ outside: Bad] And I found

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

Dear Sir I am new user of R. I am interested in modeling hydrological extreme events. I found MSClaio2008 very interesting function. In this function four criterions for choosing distributions. Can we call these criterions as model selection techniques or goodness of fit techniques or both? Because goodness of fit techniques are usually performed after modle selection. Can I found

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

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'

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?

Hi, I was wondering whether there is an implementation of the Kolmogorov-Smirnov goodness of fit test for discrete, ordinal scale data in R - I've only managed to find the test for continuous data. Thanks! Gila

Is the kolmogorov-smirnov test valid on both continuous and discrete data? I don't think so, and the example below helped me understand why. A suggestion on testing the discrete data would be appreciated. Thanks, a <- rnorm(1000, 10, 1);a # normal distribution a b <- rnorm(1000, 12, 1.5);b # normal distribution b c <- rnorm(1000, 8, 1);c # normal distribution c d <- rnorm(1000,

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

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

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

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 ) {

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.