miller.jack: The Miller Jackknife Procedure

In nonpar: A Collection of Nonparametric Hypothesis Tests

Description

This function will perform Miller's Jackknife Procedure to test differences in scale between 2 samples. It is best for large samples.

Usage

miller.jack(x, y, alpha = NULL,
alternative =c("two.sided", "greater", "less"), exact = FALSE)

Arguments

x	A vector containing data from the first sample.
y	A vector containing data from the second sample.
alpha	The Significance level, defaults to 0.05.
alternative	Defaults to two.sided. Used to determine what type of test to run.
exact	Defaults to FALSE. Used to determine whether to run the exact procedure or a large sample approximation.

Value

J	The test statistic.
Significance Level	Returns the alpha value.
P-value	Returns the p-value from Miller's Jackknife Procedure.

Author(s)

D. Lukke Sweet

References

Wiley Series in Probability and Statistics: Nonparametric Statistical Methods (3rd Edition). (2013). John Wiley & Sons.

Examples

  ## Run Miller's Jackknife Procedure on the 2 vectors.
  miller.jack(x= c(6.2, 5.9, 8.9, 6.5, 8.6),
              y = c(9.5, 9.8, 9.5, 9.6, 10.3), alpha=0.05, alternative="less")

nonpar documentation built on April 29, 2020, 9:36 a.m.