HSDEM | R Documentation |

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.

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

`alpha` |
(numeric) The p-value cutoff for determining significance. The default is |

`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 |

`formula` |
(formula) A symbolic description of the model to be fitted. |

`...` |
Additional slots and values passed to |

This object makes use of functionality from the following packages:

`emmeans`

`nlme`

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. |

Lenth R (2023).
*emmeans: Estimated Marginal Means, aka Least-Squares Means*.
R package version 1.8.4-1, https://CRAN.R-project.org/package=emmeans.

Pinheiro J, Bates D, R Core Team (2022).
*nlme: Linear and Nonlinear Mixed Effects Models*.
R package version 3.1-161, https://CRAN.R-project.org/package=nlme.

Pinheiro JC, Bates DM (2000).
*Mixed-Effects Models in S and S-PLUS*.
Springer, New York.
\Rhrefhttps://doi.org/10.1007/b98882doi:10.1007\slashb98882.

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)

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