Hi All, I want to test an H0 hypothesis about the proportions of observed counts in k classes. I know that I can do this with the chisq.test.
However, besides of the overall acceptance or rejection of the H0, I would like to know which of the k classes cause(s) rejection and I would like to know the observation-based confidence envelopes for the proportions for the k classes. My quick-and-dirty approach thus far is to do an initial chisq.test on the original k classes and then to lump data into two classes (=one of the original classes and all other original classes lumped into one new class) and do a binom.test. I interpret the result of the binom.test as indicating whether the current class might be the reason for the rejection of the overall H0. Additionally, it gives me a confidence envelope for this class. This approach seems fairly straightforward, but I just do not feel totally comfortable with it. I would feel so much better if there was something like a multinom.test, but to my knowledge there is none. Do you have any suggestions what I could rather do? For instance, I might follow a Monte Carlo-like approach: I simulate proportions for the k classes based on the proportions of observed counts with rmultinom. After exclusion of the most extreme values I construct my confidence envelope based on the remaining simulated proportions. Based on whether the hypothesized proportions fall into the observation-based confidence envelopes, I accept or reject. Do you think that either of these approaches is better or would you suggest doing something totally different? All comments and suggestions are highly appreciated. Kind regards, Michael PS: I guess my request parallels that of Matthias Schmidt from Apr 5, 2004, that was answered by Brian Ripley ... Michael Drescher Ontario Forest Research Institute Ontario Ministry of Natural Resources 1235 Queen St East Sault Ste Marie, ON, P6A 2E3 Tel: (705) 946-7406 Fax: (705) 946-2030 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.