classical_lsq: Univariate Classical Least Squares Regression

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

Description

In univariate classical least squares regression a line is fitted between each feature/variable and a response variable. The fitted line minimises the sum of squared differences between the true response and the predicted response. The coefficients (offset, gradient) of the fit can be tested for significance.

Usage

classical_lsq(alpha = 0.05, mtc = "fdr", factor_names, intercept = TRUE, ...)

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".
factor_names	(character) The name of sample meta column(s) to use.
intercept	(logical) Model intercept. Allowed values are limited to the following: "TRUE": An intercept term is included in the model. "FALSE": An intercept term is not included in the model. The default is TRUE.
...	Additional slots and values passed to struct_class.

Value

A classical_lsq object.

Examples

D = iris_DatasetExperiment()
M = classical_lsq(factor_names = 'Species')
M = model_apply(M,D)

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