On Apr 12, 2009, at 3:09 PM, jose romero wrote:
Hello list:
I generate by simulation (using different procedures) two sample
vectors of size N, each corresponding to a discrete variable and I
want to text if these samples can be considered as having the same
probability distribution (which is unknown). What is the best test
for that?
I've read that Kolmogorov-Smirnov and Anderson-Darling tests are
restricted to continuous data (http://cran.r-project.org/doc/contrib/Ricci-distributions-en.pdf
), while chi-square can handle discrete data, but how do i test (in
R) equivalence of ditribution in 2 samples using it? Are there
better tests than those i mentioned?
The question of whether two discrete samples are independent,
conditional on their joint marginals is generally handled with a chi-
square test. The theoretical distribution is only approximately chi-
square, but is seems close enough that most people will accept it.
This is not a test of "equivalence". Ricci deals with the cases where
one sample is fitted to a theoretical distribution. You do not seem to
have that situation.
?chisq.test
I find myself wondering to what purpose you are seeking these answers.
David Winsemius, MD
Heritage Laboratories
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.
