NSM3 (version 1.18)

pPairedWilcoxon: Paired Wilcoxon

Description

Function to extend wilcox.test to compute the (exact or Monte Carlo) P-value for paired Wilcoxon data in the presence of ties.

Usage

pPairedWilcoxon(x,y=NA,g=NA,method=NA,n.mc=10000)

Value

Returns a list with "NSM3Ch5p" class containing the following components:

m

number of observations in the first data group (X)

n

number of observations in the second data group (Y)

obs.stat

the observed T+ statistic

p.val

upper tail P-value

Arguments

x

Either a list or a vector containing either all or the first group of data.

y

If x contains the first group of data, y contains the second group of data. Otherwise, not used.

g

If x contains a vector of all of the data, g is a vector of 1's and 2's corresponding to group labels. Otherwise, not used.

method

Either "Exact" or "Monte Carlo", indicating the desired distribution. When method=NA, "Exact" will be used if the number of permutations is 10,000 or less. Otherwise, "Monte Carlo" will be used.

n.mc

If method="Monte Carlo", the number of Monte Carlo samples used to estimate the distribution. Otherwise, not used.

Author

Grant Schneider

Details

The data entry is intended to be flexible, so that the two groups of data can be entered in any of three ways. For data a=1,2 and b=3,4 all of the following are equivalent:

pPairedWilcoxon(x=c(1,2),y=c(3,4)) pPairedWilcoxon(x=list(c(1,2),c(3,4))) pPairedWilcoxon(x=c(1,2,3,4),g=c(1,1,2,2))

See Also

Also see stats::wilcox.test()

Examples

##Hollander-Wolfe-Chicken Example 3.1 Hamilton Depression Scale Factor IV
x <-c(1.83, .50, 1.62, 2.48, 1.68, 1.88, 1.55, 3.06, 1.30)
y <-c(0.878, .647, .598, 2.05, 1.06, 1.29, 1.06, 3.14, 1.29)

wilcox.test(y,x,paired=TRUE,alternative="less")
pPairedWilcoxon(x,y)