tcfu: The TCFU test

In HuaiyuZhang/tcftt: Two-Sample Tests for Skewed Data

The TCFU test

Description

This test is suitable for testing the equality of two-sample means for the populations having unequal variances. When the populations are not normally distributed, this test can provide better type I error control and more accurate power than a large-sample t-test using normal approximation. The critical values of the test are computed based on the Cornish-Fisher expansion of the Welch's t-statistic. The order of the Cornish-Fisher expansion is allowed to be 0, 1, or 2. More details please refer to Zhang and Wang (2020).

Usage

tcfu(x1, x2, effectSize = 0, alternative = "greater", alpha = 0.05, order = 2)

Arguments

x1	the first sample.
x2	the second sample.
effectSize	the effect size of the test. The default value is 0.
alternative	the alternative hypothesis: "greater" for upper-tailed, "less" for lower-tailed, and "two.sided" for two-sided alternative.
alpha	the significance level. The default value is 0.05.
order	the order of the Cornish-Fisher expansion.

Value

test statistic, critical value, p-value, reject decision at the given significance level.

References

Zhang, H. and Wang, H. (2020). Transformation tests and their asymptotic power in two-sample comparisons. Manuscript in review.

Examples

x1 <- rnorm(20, 1, 3)
x2 <- rnorm(21, 2, 3)
tcfu(x1, x2, alternative = 'two.sided')

HuaiyuZhang/tcftt documentation built on July 9, 2023, 2:52 a.m.