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Fisher's exact test for missing values

Source: R/prop_na_class.R
prop_na.Rd

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')