similar to: test comparing spatial point patterns?

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

Hall all, Where can I find the two sample Cramer-von Mises test in R package? Thank you. Legendy -- View this message in context: http://www.nabble.com/Two-sample-Cramer-von-Mises-test-tp20174229p20174229.html Sent from the R help mailing list archive at Nabble.com.

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 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!

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, ? 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 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, 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

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,

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

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

Dear Lists, At several stations distributed regularly in space[1], we sampled repeatedly (4 times) the abundance of organisms and measured environmental parameters. I now want to compare the spatial distribution of various species (and test wether they differ or not), or to compare the distribution of a particular organism with the distribution of some environmental variable.

Hello everyone, I am looking at the joint spatial distribution of 2 kinds of organisms (estimated on a grid of points) and want to test for significant association or dissociation. My first question is: do you know a nice technique to do that, considering that I have a limited number of points (36) but that they are repeated (4 times)? I did GLMs to test for correlations between the

I'm a new user of R and a novice user in copula R package. I want to fit 3-dimensional t copula for my trivariate data. So I used the command t.cop <- tCopula(c(0.785,0.283,0.613),dim=3,dispstr="un",df=6,df.fixed = TRUE) where c(0.785,0.283,0.613) is the correlation pattern of my data with 0.785 pearson correlation between variable 1-2, 0.283 correlation between 1-3 and 0.613

Hi all, I need to test gof of 3-dimensional t copula for my trivariate observed data set. So I used the command t.cop <- tCopula(c(0.785,0.283,0.613),dim=3,dispstr="un",df=6,df.fixed = TRUE) where c(0.785,0.283,0.613) is the correlation pattern of my data with 0.785 pearson correlation between variable 1-2, 0.283 correlation between 1-3 and 0.613 is the correlation between variable

John, thanks for starting (or restarting) this thread.? I'd like to add to the discussion with another concrete example, about as simple as it gets, which may help focus at least part of this discussion. I have worked with Taylor Arnold to implement a method developed in Conover (1972) for Kolmogorov-Smirnov goodness-of-fit tests for discrete distributions (one-sample only).? We needed this

Hi I've built a dll using Fortran code and can call it by either R or Fortran. Calling by the former gives me the wrong answer and the later gives the correct answer. >From what I've read, it looks like I should use the subroutines DBLEPR, INTPR and REALPR to print to the R console rather than using Fortran standard I/O and that if I use the command R CMD SHLIB source.f that these