R-package | compressiveRDA

• Version: 1.1

• GitHub Link: https://github.com/mntabassm/compressiveRDA

• Title: Compressive regularized discriminant analysis for simultaneous feature selection and classification of high-dimensional data, with applications to genomic studies.

• Short Title: Compressive Regularized Discriminant Analysis (CRDA)

• Authors: Muhammad Naveed Tabassum and Esa Ollila

• Maintainer: Muhammad Naveed Tabassum

• Language: R

• Date: 26.09.2018

• Date (Last update): 15.10.2019

Introduction

The compressiveRDA pacakge implements the CRDA approach whose goal is to address three facets of high-dimensional classification: namely, accuracy, computational complexity, and interpretability. The currently available competitors of CRDA method present a weak spot for at least one of the aforementioned criteria of an HD classifier.

Installation

The compressiveRDA pacakge can be installed from GitHub, using the devtools pacakge as:

devtools::install_github("mntabassm/compressiveRDA")
library(compressiveRDA)

NOTE: If there is some problem coming then, do as:

devtools::install_github("mntabassm/compressiveRDA", force = TRUE)
library(compressiveRDA)

Example

As an example, just run the function 'crda.demo()' that does the classification for one split of a real genomic dataset, Khan'2001.

basic example code

• crda.demo() : It does classification using a uniform prior.

• crda.demo(prior = 'estimated') : It does classification using a empirically estimated prior.