similar to: Two sample Cramer-von Mises test

Hi, ? I'm trying to carry out Cramer von Mises tests between pairs of vectors belonging to a discrete distribution (concretely frequencies from 0 to 200). However, the program crashes in the attempt. The problem seems to be that these vectors only have positive integer numbers (+ zero). When I add a random very small positive decimal to the non-decimal part everything works fine (files prm1

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

Hi, this is my first help request so please bear with me. I've been running some normality tests using the nortest package. For some of my datasets the Cramer-von Mises normality test generates an extremely high probability (e.g., 1.637e+31) and indicates normality when the other tests do not. Is there something I'm misunderstanding or potentially a bug in the code? Below are the

Hi all, When I detect the spatial point pattern, I want to use the Cramer-von Mises statistic to assess the curve-wise significance of deviations from null hypotheses. Who can tell me which function in R package "Spatstat" can do this work? Thanks a lot Jeff [[alternative HTML version deleted]]

Hi all, I am using "spatstat" to investigate the spatial structure of some plant populations, and I want to detect these patters with IPP and a Thomas process based on pair-correlation function. I know the function "pcfinhom" is available to characterize the IPP, but I have no idea about how to use the pcf with Thomas process? Additionally, generating simulation envelopes

Hello list, Is there any function in some package can calculate two sample Cramer-von Mises test statistic? I searched around and only find one sample version cvm.test() in nortest package. thanks, WC _________________________________________________________________ Don’t just search. Find. Check out the new MSN Search!

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 all, I'm curious if anyone has coded an Expectation-Maximization algorithm that could help me model some circular data I have. I'd like to model it as a mixture of uniform and Von Mises centered on 0, so the only free parameters is the mixing proportion and the kappa of the Von Mises. I couldn't find anything in the contributed packages that seemed to suit this purpose. Any

Dear all, I would like to fit a von mises mixture to my data using the EM Algorithm. Unfornately, existing packages (including circstats) doesn't provide such method. So, does anybody have written such code ? Big thanks, Christophe, (R newbie)

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 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-

Hi, does anybody know of a nice test to detect trend turning points in time series? Possibly with reference? Thanks, joerg

Dear All, I has posted this question on friday. I am trying to plot von Mises density on the circle. One can use dvm function from the CircStats package, by giving a set of angles, mu and kappa to plot the circular density on the line. Does any one have a macro that does it on the circle? These plots are displayed in Nick Fisher's book. Any help will be appreciated. Thanks, G. Subramaniam

Hello forum, I want to find in survey how to test V-cramer and coefficient Goodman-Kruskal gamma, to test the collinearity between varibles to enter a model. For the V-Cramer function is used with the package vcd assocstats but without considering the survey package. I appreciate the cooperation. Att. Diana Martinez [[alternative HTML version deleted]]

Hallo, All, I have a question needs your help. I just want get a sample with the original index. For example, I have a dataset, ind x 1 39 2 24 3 15 4 75 5 61 After resample, I want to get a new dataset like this (with the original index) ind x 3 15 5 61 1 39 Thank you in advance. Legendy -- View this message in context:

Greetings. For R gurus this may be a no brainer, but I could not find pointers to efficient computation of this beast in past help files. Background - I wish to implement a Cramer-von Mises type test statistic which involves double sums of max(X_i,Y_j) where X and Y are vectors of differing length. I am currently using ifelse pointwise in a vector, but have a nagging suspicion that there is a

I believe there to be a bug in the cvm.test module of the nortest package authored by Juergen Gross. I do not know how to contact the author directly. I've been running some normality tests using the nortest package. For some of my datasets the Cramer-von Mises normality test generates an extremely high probability (e.g., 1.637e+31) and indicates normality when the other tests do

Hi, I am running the following code based on the cpm vignette's code. I believe the code is syntactically correct but it just seems to hang R. I can get this to run if I set the sims to 100 but with 2000 it just hangs. Any ideas why? Thanks, Chris library(cpm) cpmTypes <- c("Kolmogorov-Smirnov","Mann-Whitney","Cramer-von-Mises") changeMagnitudes <- c(1, 2,

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

Hello, While doing test of normality under R and SAS, in order to prove the efficiency of R to my company, I notice that Anderson Darling, Cramer Van Mises and Shapiro-Wilk tests results are quite the same under the two environnements, but the Kolmogorov-smirnov p-value really is different. Here is what I do: > ks.test(w,pnorm,mean(w),sd(w)) One-sample Kolmogorov-Smirnov test data: w D

Hi all, apologies for seeking advice on a general stats question. I ve run normality tests using 8 different methods: - Lilliefors - Shapiro-Wilk - Robust Jarque Bera - Jarque Bera - Anderson-Darling - Pearson chi-square - Cramer-von Mises - Shapiro-Francia All show that the null hypothesis that the data come from a normal distro cannot be rejected. Great. However, I don't think it looks