mixed_effect: Mixed effects model

In structToolbox: Data processing & analysis tools for Metabolomics and other omics

Description

A mixed effects model is an extension of ANOVA where there are both fixed and random effects.

Usage

mixed_effect(alpha = 0.05, mtc = "fdr", formula, ss_type = "marginal", ...)

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.
ss_type	(character) Sum of squares type. Allowed values are limited to the following: "marginal": Type III sum of squares. "sequential": Type II sum of squares. The default is "marginal".
...	Additional slots and values passed to struct_class.

Details

This object makes use of functionality from the following packages:

• nlme

• emmeans

Value

A mixed_effect object.

References

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2020). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-149, https://CRAN.R-project.org/package=nlme.

Lenth R (2020). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.5.2-1, https://CRAN.R-project.org/package=emmeans.

Fox J, Weisberg S (2019). An R Companion to Applied Regression, Third edition. Sage, Thousand Oaks CA. https://socialsciences.mcmaster.ca/jfox/Books/Companion/.

Examples

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

structToolbox documentation built on Nov. 8, 2020, 6:54 p.m.