diproperm: An R Package for the DiProPerm Test

08/30/2020
by   Andrew G. Allmon, et al.
0

High-dimensional low sample size (HDLSS) data sets emerge frequently in many biomedical applications. A common task for analyzing HDLSS data is to assign data to the correct class using a classifier. Classifiers which use two labels and a linear combination of features are known as binary linear classifiers. The direction-projection-permutation (DiProPerm) test was developed for testing the difference of two high-dimensional distributions induced by a binary linear classifier. This paper discusses the key components of the DiProPerm test, introduces the diproperm R package, and demonstrates the package on a real-world data set.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
02/03/2016

High-Dimensional Regularized Discriminant Analysis

Regularized discriminant analysis (RDA), proposed by Friedman (1989), is...
research
06/21/2020

The classification for High-dimension low-sample size data

Huge amount of applications in various fields, such as gene expression a...
research
09/10/2020

Population structure-learned classifier for high-dimension low-sample-size class-imbalanced problem

The Classification on high-dimension low-sample-size data (HDLSS) is a c...
research
06/06/2018

On high-dimensional modifications of some graph-based two-sample tests

Testing for the equality of two high-dimensional distributions is a chal...
research
06/19/2021

Fasano-Franceschini Test: an Implementation of a 2-Dimensional Kolmogorov-Smirnov test in R

The univariate Kolmogorov-Smirnov (KS) test is a non-parametric statisti...
research
01/05/2019

Population-Guided Large Margin Classifier for High-Dimension Low -Sample-Size Problems

Various applications in different fields, such as gene expression analys...
research
09/21/2021

PKLM: A flexible MCAR test using Classification

We develop a fully non-parametric, fast, easy-to-use, and powerful test ...

Please sign up or login with your details

Forgot password? Click here to reset