Performs exact one sample and two sample Wilcoxon tests on vectors of data.
wilcoxE.test(x, y = NULL, mu = 0, paired = FALSE, alternative = "two.sided", conf.level = 0.95)
| x | is a numeric vector of data values. Non-finite (i.e. infinite or missing) values will be omitted. |
|---|---|
| y | an optional numeric vector of data values |
| mu | a number specifying an optional parameter used to form the null hypothesis |
| paired | a logical indicating whether you want a paired test |
| alternative | a character string specifying the alternative hypothesis,
must be one of |
| conf.level | confidence level of the interval |
A list with class htest containing the following components:
the value of the test statistic with a name describing it
the p-value for the test
the location
parameter mu
a character string describing the alternative hypothesis
the type of test applied
a character string giving the names of the data
a confidence interval for the location parameter
an estimate of the location parameter
If only x is given, or if both x and y are given and
paired="TRUE", a Wilcoxon signed rank test of the null hypothesis
that the distribution of x (in the one sample case) or of x -
y (in the paired two sample case) is symmetric about mu is
performed.
Otherwise, if both x and y are given and
paired="FALSE", a Wilcoxon rank sum test is done. In this case, the
null hypothesis is that the distributions of x and y differ by
a location shift of mu and the alternative is that they differ by
some other location shift (and the one-sided alternative "greater" is
that x is shifted to the right of y).
The function is rather primitive and should only be used for problems with fewer than 19 observations as the memory requirements are rather large.
Gibbons, J.D. and Chakraborti, S. (1992). Nonparametric Statistical Inference. Marcel Dekker Inc., New York.
Myles Hollander & Douglas A. Wolfe (1999), Nonparametric Statistical Inference. New York: John Wiley & Sons.
wilcox.test
# Wilcoxon Signed Rank Test - Example 10.3 PH <- c(7.2,7.3,7.3,7.4) wilcoxE.test(PH, mu=7.25, alternative="greater")#> #> Wilcoxon Signed Rank Test #> #> data: PH #> t+ = 8, p-value = 0.25 #> alternative hypothesis: true median is greater than 7.25 #> 93.75 percent confidence interval: #> 7.25 Inf #> sample estimates: #> (pseudo)median #> 7.3 #># Wilcoxon Signed Rank Test (Dependent Samples) - Example 10.5 part c. with(data = Aggression, wilcoxE.test(violence,noviolence,paired=TRUE,alternative="greater"))#> #> Wilcoxon Signed Rank Test (Dependent Samples) #> #> data: violence and noviolence #> t+ = 118.5, p-value = 0.003265 #> alternative hypothesis: true median difference is greater than 0 #> 95.20569 percent confidence interval: #> 2 Inf #> sample estimates: #> (pseudo)median #> 4.5 #># Wilcoxon Rank Sum Test - Example 10.7 x <- c(7.2,7.2,7.3,7.3) y <- c(7.3,7.3,7.4,7.4) wilcoxE.test(x,y)#> #> Wilcoxon Rank Sum Test #> #> data: x and y #> w = 12, p-value = 0.1714 #> alternative hypothesis: true median is not equal to 0 #> 82.85714 percent confidence interval: #> -0.2 0.0 #> sample estimates: #> difference in location #> -0.1 #>rm(PH, x, y)