Nonparametric Tests of Group Differences
R provides functions for carrying out Mann-Whitney U, Wilcoxon Signed Rank, Kruskal Wallis, and Friedman tests.# independent 2-group Mann-Whitney U Test
wilcox.test(y~A)
# where y is numeric and A is A binary factor
# independent 2-group Mann-Whitney U Test
wilcox.test(y,x) # where y and x are numeric
# dependent 2-group Wilcoxon Signed Rank Test
wilcox.test(y1,y2,paired=TRUE) # where y1 and y2 are numeric
# Kruskal Wallis Test One Way Anova by Ranks
kruskal.test(y~A) # where y1 is numeric and A is a factor
# Randomized Block Design - Friedman Test
friedman.test(y~A|B)
# where y are the data values, A is a grouping factor
# and B is a blocking factor
For the wilcox.test you can use the alternative="less" or alternative="greater" option to specify a one tailed test.
Parametric and resampling alternatives are available.
The package npmc provides nonparametric multiple comparisons.
library(npmc)
npmc(x)
#
where x is a dataframe containing variable 'var'
#
(response variable) and 'class' (grouping variable)
Visualizing Results
Use box plots or density plots to visual group differences.