Many thanks Peter and David, Jorge Ivan Velez gave me just what Peter suggests: a resampling solutionÂ… which is perfect.
I was surprised this is not available as a packaged and documented test: perhaps an opportunity for a helpful paper for someone enterprising. Best, tim On Aug 7, 2011, at 7:24 PM, peter dalgaard wrote: > > On Aug 7, 2011, at 20:05 , David Winsemius wrote: > >> >> On Aug 6, 2011, at 1:19 PM, Timothy Bates wrote: >> >>> Dear R-users, >>> I am comparing differences in variance, skew, and kurtosis between two >>> groups. >>> >>> For variance the comparison is easy: just >>> >>> var.test(group1, group2) >>> >>> I am using agostino.test() for skew, and anscombe.test() for kurtosis. >>> However, I can't find an equivalent of the F.test or Mood.test for >>> comparing kurtosis or skewness between two samples. >> >> What are you planning on doing with these "moment-ous" tests? Most questions >> to this list about "how to test for normality" are based on false >> probabilistic premises promulgated by pendantic poseurs. >> >> (Not that I am above pendantry, myself.) >> >>> >>> Would the test just be a 1 df test on the difference in Z or F scores >>> returned by the agostino or anscombe? How are the differences distributed: >>> chi2? >>> >>> Any guidance greatly appreciated. >> >> It shouldn't be too difficult to construct a normal theory test using the >> distributional results for third and fourth sample moments at the Wikipedia >> Page for D'Agostino's test: >> >> http://en.wikipedia.org/wiki/D%27Agostino%27s_K-squared_test >> > > But the trouble is that those results are valid for normal samples. This is > fine if you are testing for normality, but the issue was how to compare skew > and kurtosis between two arbitrary distributions, and in those cases the > distribution of the sample cumulants depends on even higher moments of the > distributions. So presumably, you need to go to resampling techniques > (bootstrap/jackknife). > >> A statistic could be formed for two sample values with expected difference >> of zero and equal variances that depend on sample size : >> >> (k1 - k2)/sqrt(var1 +var2) >> > > > > >> >> Or you could use the distributional results offered in: >> >> Looney, S. W. (1995). How to use tests for univariate nor- >> mality to assess multivariate normality. American Statis- >> tician, 49, 64-70. >> >> >> -- >> David. >> >> >> >>> >>> google and wikipedia return hits for measuring the third and fourth >>> standardized moments, but none I can see for comparing differences on these >>> parameters. >>> >>> best, tim >>> ______________________________________________ >>> 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. >> >> David Winsemius, MD >> West Hartford, CT >> >> ______________________________________________ >> 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. > > -- > Peter Dalgaard, Professor, > Center for Statistics, Copenhagen Business School > Solbjerg Plads 3, 2000 Frederiksberg, Denmark > Phone: (+45)38153501 > Email: pd....@cbs.dk Priv: pda...@gmail.com > "Døden skal tape!" --- Nordahl Grieg > > > > > > > [[alternative HTML version deleted]]
______________________________________________ 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.
