prop_na: Fisher's exact test for missing values

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

Description

A Fisher's exact test is used to compare the number of missing values in each group. Multiple test corrected p-values are computed to indicate whether there is a significant difference in the number of missing values across groups for each feature.

Usage

prop_na(alpha = 0.05, mtc = "fdr", factor_name, ...)

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.
...	Additional slots and values passed to struct_class.

Value

A prop_na object.

struct object

Examples

M = prop_na(factor_name='Species')

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