| tcfu {tcftt} | R Documentation |
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')