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Hi, I would need to extend the Friedman test to a replicated design. Currently the function: friedman.test(y, ...) only works for unreplicated designs. I found in Conover 1999 "Practical Nonparamteric statistics" an extension of the formula to my case. Nevertheless, other sources, like Sheskin 2000 "Parametric and Nonparametric statistical Procedures" and Daniel 1990 "Applied nonparametric statistics" give a different formula from that of Conover in the unreplicated case. Since they do not provide the extension of this formula to the replicated case I would like to kn...

Hi! I would like to conduct a Cochran`s Q Test in R, but have not found any suitable function. My first idea was: J <- as.table(matrix(c(6,16,2,4),ncol=2, dimnames = list("C" = c("Favorable","Unfavorable"),"Drug A Favorable"=c("B Favorable","B Unfavorable")))) L <- as.table(matrix(c(2,4,6,6),ncol=2, dimnames =

...st). If significant, the group with the smaller rank sum has greater variability. References: Sidney Siegel and John Wilder Tukey (1960) "A nonparametric sum of ranks procedure for relative spread in unpaired samples." Journal of the American Statistical Association. See also, David J. Sheskin (2004) "Handbook of parametric and nonparametric statistical procedures." 3rd edition. Chapman and Hall/CRC. Boca Raton, FL. Notes: The Siegel-Tukey test has relatively low power and may, under certain conditions, indicate significance due to differences in medians rather than difference...