wilcoxmed (version 0.0.1)

Wilcox.m.test: 1-Sample Wilcoxon Sign Rank Hypothesis Test for Medians

Description

This function allows the user to conduct the 1-Sample Wilcoxon Sign Rank Hypothesis Test for Medians using the probability values from the exact distribution of W.

Usage

Wilcox.m.test(dat, m_h0, alpha = 0.05,
alternative=c('greater', 'lesser', 'noteq'), normal_approx=FALSE)

Arguments

dat

data vector relating to the sample the user is performing the hypothesis test for

m_h0

The value of the median as specified by the null hypothesis H_0

alpha

The significance level of the hypothesis test (default = 0.05)

alternative

The sign of the alternative hypothesis. e.g 'greater' - H_1:m>m_h0 , 'lesser' - H_1:m<m_h0, 'noteq' - H_1:m!=m_h0

normal_approx

Should the normal approximation test be applied? (default = FALSE)

Value

Prints out the results of the tests, and returns 3 values- test statistic, p-value, and the significance level of the test, alpha

Details

This hypothesis test allows breaking of ties, and the number of ties broken is also reflected in the printed results.

References

Peter J. Bickel and Kjell A. Doksum (1973). Mathematical Statistics: Basic Ideas and Selected Topics. Prentice Hall.

See Also

wilcox.test for the same tests applied to 2 sample problems but is not able to break ties

Examples

# NOT RUN {
##Given some data: 3, 4, 7, 10, 4, 12, 1, 9, 2, 15
##If we want to test the hypotheses H_0: m=5 against H_1: m>5
##without using normal approximation:
vec = c(3, 4, 7, 10, 4, 12, 1, 9, 2, 15)
res = Wilcox.m.test(dat = vec, m_h0 = 5,
alternative = 'greater', normal_approx = FALSE)

##If we want to apply the normal approximation(Z-test), with the same hypotheses:
res = Wilcox.m.test(dat = vec, m_h0 = 5,
alternative = 'greater', normal_approx = TRUE)
# }