TOPIC My question regards the philosophy behind how R implements corrections to chi-square statistical tests. At least in recent versions (I'm using 2.13.1 (2011-07-08) on OSX 10.6.8.), the chisq.test function applies the Yates continuity correction for 2 by 2 contingency tables. But when used as a goodness of fit test (GoF, aka likelihood ratio test), chisq.test does not appear to implement any corrections for widely recognized problems, such as small sample size, non-uniform expected frequencies, and one D.F.
>From the help page: "In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by p, each sample being of size n = sum(x)." Is the thinking that random sampling completely obviates the need for corrections? Wouldn't the same statistical issues still apply (e.g. poor continuity approximation with one D.F., problems with non-uniform expected frequencies, etc) with random sampling? Regards, Mike ==================== Michael M. Fuller, Ph.D. Department of Biology University of New Mexico Albuquerque, NM ______________________________________________ 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.
