NSM3 (version 1.11)

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)

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.

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

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

# NOT RUN {
##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)
# }