brunner.munzel.test {lawstat} | R Documentation |
The Brunner–Munzel test for stochastic equality of two samples,
which is also known as the Generalized Wilcoxon test.
NA
s from the data are omitted.
brunner.munzel.test(
x,
y,
alternative = c("two.sided", "greater", "less"),
alpha = 0.05
)
x |
the numeric vector of data values from the sample 1. |
y |
the numeric vector of data values from the sample 2. |
alternative |
a character string specifying the alternative hypothesis,
must be one of |
alpha |
significance level, default is 0.05 for 95% confidence interval. |
There exist discrepancies with Brunner and Munzel (2000) because there is a typo in the paper. The corrected version is in Neubert and Brunner (2007) (e.g., compare the estimates for the case study on pain scores). The current function follows Neubert and Brunner (2007).
A list of class "htest"
with the following components:
statistic |
the Brunner–Munzel test statistic. |
parameter |
the degrees of freedom. |
conf.int |
the confidence interval. |
p.value |
the |
data.name |
a character string giving the name of the data. |
estimate |
an estimate of the effect size, i.e., |
Wallace Hui, Yulia R. Gel, Joseph L. Gastwirth, Weiwen Miao. This function was updated with the help of Dr. Ian Fellows.
Brunner E, Munzel U (2000).
“The nonparametric Behrens–Fisher problem: asymptotic theory and a small-sample approximation.”
Biometrical Journal, 42(1), 17–25.
Neubert K, Brunner E (2007).
“A studentized permutation test for the non-parametric Behrens–Fisher problem.”
Computational Statistics & Data Analysis, 51(10), 5192–5204.
doi:10.1016/j.csda.2006.05.024.
## Pain score on the third day after surgery for 14 patients under
## the treatment Y and 11 patients under the treatment N
## (see Brunner and Munzel, 2000; Neubert and Brunner, 2007).
Y <- c(1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 4, 1, 1)
N <- c(3, 3, 4, 3, 1, 2, 3, 1, 1, 5, 4)
brunner.munzel.test(Y, N)
## Brunner-Munzel Test
## data: Y and N
## Brunner-Munzel Test Statistic = 3.1375, df = 17.683, p-value = 0.005786
## 95 percent confidence interval:
## 0.5952169 0.9827052
## sample estimates:
## P(X<Y)+.5*P(X=Y)
## 0.788961