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Tukey's Honest Significant Difference using estimated marginal means

Source: R/HSDEM_class.R
HSDEM.Rd

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. For mixed effects models estimated marginal means are used.

Usage

HSDEM(alpha = 0.05, mtc = "fdr", formula, ...)

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.

...

Additional slots and values passed to struct_class.

Value

A HSDEM object with the following output slots:

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.

Details

This object makes use of functionality from the following packages:

  • emmeans

  • nlme

Inheritance

A HSDEM object inherits the following struct classes:

[HSDEM] >> [model] >> [struct_class]

References

Lenth R (2024). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.10.1, https://CRAN.R-project.org/package=emmeans.

Pinheiro J, Bates D, R Core Team (2023). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-164, https://CRAN.R-project.org/package=nlme.

Pinheiro JC, Bates DM (2000). Mixed-Effects Models in S and S-PLUS. Springer, New York. doi:10.1007/b98882 https://doi.org/10.1007/b98882.

Examples

M = HSDEM(
      alpha = 0.05,
      mtc = "fdr",
      formula = y ~ x)

D = iris_DatasetExperiment()
D$sample_meta$id=rownames(D) # dummy id column
M = HSDEM(formula = y~Species+ Error(id/Species))
M = model_apply(M,D)