fastJT-package: Efficient Jonckheere-Terpstra Test Statistics for Robust...

Description Details Author(s) References See Also Examples

Description

This Rcpp-based package implements highly efficient functions for the calculation of the Jonkheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages OpenMP directives for multi-core computing to reduce overall processing time.

Details

Package: fastJT
Type: Package
Version: 1.0.4
Date: 2017-08-14
License: GPL-3

Please see the example function calls below, or refer to the individual function documentation or the included vignette for more information. The package vignette serves as a tutorial for using this package. The technical details are provided in the reference cited below. Specifically, the calculation of the standardized test statistics employs the null variance equation as defined by Hollander and Wolfe (1999, eq. 6.19) to account for ties in the data.

Author(s)

Jiaxing Lin, Alexander Sibley, Ivo Shterev, and Kouros Owzar

Maintainer: Jiaxing Lin <[email protected]>

References

Hollander, M. and Wolfe, D. A. (1999) Nonparametric Statistical Methods. New York: Wiley, 2nd edition.

See Also

Rcpp

Examples

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# Generate dummy data	
num_sample  <- 100
num_marker  <- 10
num_feature <- 500
set.seed(12345)
Data <- matrix(rnorm(num_sample*num_marker), num_sample, num_marker)
Features <- matrix(rbinom(num_sample*num_feature, 2, 0.5), num_sample, num_feature)
colnames(Data) <- paste0("Var:",1:num_marker)
colnames(Features) <- paste0("Ftr:",1:num_feature)
   
res <- fastJT(Y=Data, X=Features, outTopN=15)
res
res <- fastJT.select(Y=Data, X=Features, cvMesh=NULL, kFold=5, 
                     selCrit=NULL, outTopN=5, numThreads=1)
res

fastJT documentation built on Aug. 15, 2017, 1:01 a.m.