prop_na: Fisher's exact test for missing values

In computational-metabolomics/structToolbox: Data processing & analysis tools for Metabolomics and other omics

Fisher's exact test for missing values

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 with the following output slots:

p_value	(data.frame) The probability of observing the calculated statistic.
significant	(data.frame) TRUE if the calculated p-value is less than the supplied threshold (alpha).
na_count	(data.frame) The number of NA values per group of the chosen factor.

struct object

Inheritance

A prop_na object inherits the following struct classes:

⁠[prop_na]⁠ >> ⁠[model]⁠ >> ⁠[struct_class]⁠

Examples

M = prop_na(
      alpha = 0.05,
      mtc = "fdr",
      factor_name = "V1")

M = prop_na(factor_name='Species')

computational-metabolomics/structToolbox documentation built on Feb. 12, 2024, 2:15 a.m.