fisher_exact: Fisher Exact Test

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

Description

A fisher exact test is used to analyse contingency tables by comparing the number of correctly/incorrectly predicted group labels. A multiple test corrected p-value indicates whether the number of measured values is significantly different between groups.

Usage

fisher_exact(alpha = 0.05, mtc = "fdr", factor_name, factor_pred, ...)

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_name	(character) The name of a sample-meta column to use.
factor_pred	(data.frame) A data.frame, where each column is a factor of predicted group labels to compare with the true groups labels.
...	Additional slots and values passed to struct_class.

Value

A fisher_exact object.

Examples

# load some data
D=MTBLS79_DatasetExperiment()

# prepare predictions based on NA
pred=as.data.frame(is.na(D$data))
pred=lapply(pred,factor,levels=c(TRUE,FALSE))
pred=as.data.frame(pred)

# apply method
M = fisher_exact(alpha=0.05,mtc='fdr',factor_name='class',factor_pred=pred)
M=model_apply(M,D)

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