Learn R Programming

multtest (version 2.28.0)

Resampling-based multiple hypothesis testing

Description

Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments.

Copy Link

Version

Monthly Downloads

231

Version

2.28.0

License

LGPL

Last Published

February 15th, 2017

Functions in multtest (2.28.0)

MTP-class

Class "MTP", classes and methods for multiple testing procedure output
MTP-methods

Methods for MTP and EBMTP objects in Package `multtest'
corr.null

Function to estimate a test statistics joint null distribution for t-statistics via the vector influence curve
boot.null

Non-parametric bootstrap resampling function in package `multtest'
ss.maxT

Procedures to perform multiple testing
MTP

A function to perform resampling-based multiple hypothesis testing
multtest-internal

Internal multtest functions and variables
meanX

Functions to create test statistic closures and apply them to data
wapply

Weighted version of the apply function
mt.maxT

Step-down maxT and minP multiple testing procedures
mt.plot

Plotting results from multiple testing procedures
mt.rawp2adjp

Adjusted p-values for simple multiple testing procedures
mt.reject

Identity and number of rejected hypotheses
EBMTP-class

Class "EBMTP", classes and methods for empirical Bayes multiple testing procedure output
fwer2gfwer

Function to compute augmentation MTP adjusted p-values
get.index

Function to compute indices for ordering hypotheses in Package 'multtest'
golub

Gene expression dataset from Golub et al. (1999)