Marc Schwartz <marc_schwa...@me.com> [Tue, Dec 28, 2010 at 07:14:49PM CET]: [...] > > An old question of mine: Is there any reason not to use binom.test() > > other than historical reasons? >
(I meant "in lieu of the McNemar approximation", sorry if some misunderstanding ensued). > I may be missing the context of your question, but I frequently see > exact binomial tests being used when one is comparing the > presumptively known probability of some dichotomous characteristic > versus that which is observed in an independent sample. For example, > in single arm studies where one is comparing an observed event rate > against a point estimate for a presumptive historical control. In the McNemar context (as used by SPSS) the null hypothesis is p=0.5. > I also see the use of exact binomial (Clopper-Pearson) confidence > intervals being used when one wants to have conservative CI's, given > that the nominal coverage of these are at least as large as > requested. That is, 95% exact CI's will be at least that large, but > in reality can tend to be well above that, depending upon various > factors. This is well documented in various papers. Confidence intervals are not that regularly used in the McNemar context, as the conditional probability "a > b given they are unequal" is not that much an interpretable quantity as is the event probability in a single arm study. > I generally tend to use Wilson CI's for binomial proportions when reporting analyses. I have my own code but these are implemented in various R functions, including Frank's binconf() in Hmisc. Thanks for the hint. -- Johannes Hüsing There is something fascinating about science. One gets such wholesale returns of conjecture mailto:johan...@huesing.name from such a trifling investment of fact. http://derwisch.wikidot.com (Mark Twain, "Life on the Mississippi") ______________________________________________ R-help@r-project.org 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.