Tukey's HSD post hoc test is a modified t-test applied for all features to all pairs of levels in a factor. It is used to determine which groups are different (if any). A multiple test corrected p-value is computed to indicate which groups are significantly different to the others for each feature.
Arguments
- alpha
(numeric) The p-value cutoff for determining significance. The default is
0.05.- mtc
(character) Multiple test correction method. Allowed values are limited to the following:
"bonferroni": Bonferroni correction in which the p-values are multiplied by the number of comparisons."fdr": Benjamini and Hochberg False Discovery Rate correction."none": No correction.
The default is
"fdr".- formula
(formula) A symbolic description of the model to be fitted.
- unbalanced
(logical) Unbalanced model. Allowed values are limited to the following:
"TRUE": A correction is applied for unbalanced designs."FALSE": No correction is applied for unbalanced designs.
The default is
FALSE.- ...
Additional slots and values passed to
struct_class.
Value
A HSD object with the following output slots:
difference | (data.frame) |
UCL | (data.frame) |
LCL | (data.frame) |
p_value | (data.frame) The probability of observing the calculated statistic if the null hypothesis is true. |
significant | (data.frame) True/False indicating whether the p-value computed for each variable is less than the threshold. |
References
de Mendiburu F (2023). agricolae: Statistical Procedures for Agricultural Research. R package version 1.3-7, https://CRAN.R-project.org/package=agricolae.
Examples
M = HSD(
alpha = 0.05,
mtc = "fdr",
formula = y ~ x,
unbalanced = FALSE)
D = iris_DatasetExperiment()
M = HSD(formula=y~Species)
M = model_apply(M,D)