Spencer,
Thank you for referring me to your other email on Exact goodness-of-fit test. However, I'm not entirely sure if what you mentioned is the same for my case. I'm not a statistician and it would help me if you could explain what you meant in a little more detail. Perhaps I need to explain the problem in more detail. I am looking for a way to calculate exaxt p-values by Monte Carlo Simulation for Durbin's test. Durbin's test statistic is similar to Friedman's statistic, but considers the case of Balanced Incomplete block designs. I have found a function written by Felipe de Mendiburu for calculating Durbin's statistic, which gives the chi-squared p-value. I have also been read an article by Torsten Hothorn "On exact rank Tests in R" (R News 1(1), 11–12.) and he has shown how to calculate Monte Carlo p-values using pperm. In the article by Torsten Hothorn he gives: R> pperm(W, ranks, length(x)) He compares his method to that of StatXact, which is the program Rayner and Best suggested using. Is there a way to do this for example for the friedman test. A paper by Joachim Rohmel discusses "The permutation distribution for the friendman test" (Computational Statistics & Data Analysis 1997, 26: 83-99). This seems to be on the lines of what I need, although I am not quite sure. Has anyone tried to recode his APL program for R? I have tried a number of things, all unsucessful. Searching through previous postings have not been very successful either. It seems that pperm is the way to go, but I would need help from someone on this. Any hints on how to continue would be much appreciated. Peter Spencer Graves wrote: >Hi, Peter: > > Please see my reply of a few minutes ago subject: exact >goodness-of-fit test. I don't know Rayner and Best, but the same >method, I think, should apply. spencer graves > >Peter Ho wrote: > > > >>HI R-users, >> >>I am trying to repeat an example from Rayner and Best "A contingency >>table approach to nonparametric testing (Chapter 7, Ice cream example). >> >>In their book they calculate Durbin's statistic, D1, a dispersion >>statistics, D2, and a residual. P-values for each statistic is >>calculated from a chi-square distribution and also Monte Carlo p-values. >> >>I have found similar p-values based on the chi-square distribution by >>using: >> >> > pchisq(12, df= 6, lower.tail=F) >>[1] 0.0619688 >> > pchisq(5.1, df= 6, lower.tail=F) >>[1] 0.5310529 >> >>Is there a way to calculate the equivalent Monte Carlo p-values? >> >>The values were 0.02 and 0.138 respectively. >> >>The use of the approximate chi-square probabilities for Durbin's test >>are considered not good enough according to Van der Laan (The American >>Statistician 1988,42,165-166). >> >> >>Peter >>-------------------------------- >>ESTG-IPVC >> >>______________________________________________ >>[email protected] mailing list >>https://stat.ethz.ch/mailman/listinfo/r-help >>PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >> >> > > > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
