corrected.z: Looney and Jones's Corrected Z-Test
In Garylikai/PMLi-1.0-R-Package: AMS 597 Project Partially Matched Samples R Package

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/corrected.z.R

corrected.z is used to perform Looney and Jones's corrected Z-test for partially matched samples, specified by giving a data frame or matrix, testing hypothesis, true mean and confidence level.

corrected.z(
  data,
  alternative = c("two.sided", "less", "greater"),
  mu = 0,
  conf.level = 0.95,
  ...
)

`data`	a 2-by-n or n-by-2 partially matched pairs samples data frame or matrix.
`alternative`	a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.
`mu`	a number indicating the true difference in means.
`conf.level`	confidence level for the returned confidence interval.
`...`	further arguments to be passed to or from methods.

Whether the dataset input is partially matched samples will be checked. If not, appropriate hypothesis tests, such as one and two sample t-tests, will be performed instead.

A list with class "htest" containing the following components:

`statistic`	the value of the test statistic.
`parameter`	the degrees of freedom of the test statistic or NA.
`p.value`	the p-value of the test.
`conf.int`	a confidence interval for the mean appropriate to the specified alternative hypothesis.
`estimate`	the estimated mean or difference in means.
`null.value`	the specified hypothesized value of the mean or mean difference.
`stderr`	the standard error of the mean (difference).
`alternative`	a character string describing the alternative hypothesis.
`method`	a character string indicating what type of test was performed.
`data.name`	a character string giving the name(s) of the data.

Kai Li kai.li@stonybrook.edu

Kuan P F, Huang B. A simple and robust method for partially matched samples using the p-values pooling approach. Statistics in medicine. 2013; 32(19): 3247-3259.

Looney S, Jones P. A method for comparing two normal means using combined samples of correlated and uncorrelated data. Statistics in Medicine. 2003; 22(9):1601-1610. [PubMed: 12704618]

t.test for the one and two-sample t-tests.

var.test for the F test to compare two variances.

shapiro.test for the Shapiro-Wilk test of normality.

wilcox.test for the one and two-sample Wilcoxon tests.

weighted.z, modified.t, mle.hetero, and mle.homo for other statistical approaches for partially matched samples.

# pm is a sample dataset for the PMLi package

# Looney and Jones's corrected Z-test formula interface
corrected.z(pm, "less", conf.level = 0.99)

# p-value of Looney and Jones's corrected Z-test
p.value1 <- corrected.z(pm)$p.value