| Title: | Tests for Tree Ordered Alternatives in One-Way ANOVA |
|---|---|
| Description: | Implements a likelihood ratio test and two pairwise standerdized mean difference based tests for testing equality of means against tree ordered alternatives in one-way ANOVA. The null hypothesis assumes all group means are equal, while the alternative assumes the control mean is less than or equal to each treatment mean with at least one strict inequality. The input is a list of numeric vectors for each group and a significance level. The output provides the test statistic, critical value, and the test decision. |
| Authors: | Subha Halder [aut, cre] |
| Maintainer: | Subha Halder <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.0 |
| Built: | 2026-05-24 09:19:30 UTC |
| Source: | https://github.com/subhahalder-spec/treeordertests |
Performs a likelihood ratio test for testing the equality of means against tree ordered alternatives.
TreeLRT(sample_data, significance_level)TreeLRT(sample_data, significance_level)
sample_data |
A list of numeric vectors. The first element represents the control group, the others represent treatment groups. |
significance_level |
A numeric value between 0 and 1 specifying the significance level for the test (e.g., 0.05). |
This test compares the null hypothesis of equal means for all groups to the alternative that the control group mean is less than or equal to the treatment group means under the tree order restriction.
The likelihood ratio statistic is computed using constrained maximum likelihood estimates under the null and tree ordered alternative hypotheses. The critical value is estimated by a bootstrap procedure.
A character string with the critical value, the LRT test statistic, and the test decision.
Subha Halder
Computes a test statistic based on the maximum standardized difference between the treatment means and the control mean under the tree order restriction.
TreeMaxD(sample_data, significance_level)TreeMaxD(sample_data, significance_level)
sample_data |
A list of numeric vectors. The first element represents the control group, the others represent treatment groups. |
significance_level |
A numeric value between 0 and 1 specifying the significance level for the test (e.g., 0.05). |
The test statistic is the maximum of standardized differences between each treatment mean and the control mean. The critical value is estimated by a bootstrap procedure.
A character string with the critical value, the Max-D test statistic, and the test decision.
Subha Halder
Computes a test statistic based on the minimum standardized difference between the treatment means and the control mean under the tree order restriction.
TreeMinD(sample_data, significance_level)TreeMinD(sample_data, significance_level)
sample_data |
A list of numeric vectors. The first element represents the control group, the others represent treatment groups. |
significance_level |
A numeric value between 0 and 1 specifying the significance level for the test (e.g., 0.05). |
The test statistic is the minimum of standardized differences between each treatment mean and the control mean. The critical value is estimated by a bootstrap procedure.
A character string with the critical value, the Min-D test statistic, and the test decision.
Subha Halder