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
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
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David Winsemius, MD
West Hartford, CT
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