p_methods: P-value Corrections

In UBC-MDS/ptoolkit: A toolkit for adjusting and visualizing p-values coming from multiple tests

Description

Dataframe: appends to input dataframe both adjusted p-values and significance levels (Bonferroni and BH) in ascending raw p-value order. Includes following columns: - bonf_value (numeric): Bonferroni adjusted significance level (same for all) - bonf_significant (logical): True if significant p-value or False if not - bh_value (numeric): Benjamini-Hochberg (BH) critical value - BH_significant (logical): True if significant p-value or False if not

Usage

p_methods(data, pv_index, alpha = 0.05)

Arguments

data	(dataframe) dataframe containing at least a column of p-values to be adjusted
pv_index	(numeric) if the input is a dataframe, col refers to the column name of the dataframe that has the p-values
alpha	(numeric) significance level for both methods

Details

Requirements: Dplyr

Value

data.frame

UBC-MDS/ptoolkit documentation built on May 25, 2019, 1:36 p.m.