README.md

In Pennisetum/JACA: Joint Association and Classification Analysis

JACA: Joint association and classification analysis of multi-view data

The R package 'JACA' implements a joint statistical framework that performs association and classification analysis for multi-view data, where multi-view data refers to matched sets of measurements on the same subjects. The corresponding reference is

"Joint association and classification analysis of multi-view data" by Zhang and Gaynanova (2018+).

Installation

To install the latest version from Github, use

library(devtools)
devtools::install_github("Pennisetum/JACA")

Usage

```s library(JACA)

Example

set.seed(1)

Generate class indicator matrix Z

n = 100 Z=matrix(c(rep(1, n),rep(0, 2 * n)), byrow = FALSE, nrow = n) for(i in 1:n){ Z[i, ] = sample(Z[i, ]) }

Generate input data X_list

d = 2 X_list = sapply(1:d, function(i) list(matrix(rnorm(n * 20), n, 20)))

Train JACA model

W = jacaTrain(Z, X_list, lambda = rep(0.05, 2), verbose = FALSE, alpha= 0.5, rho = 0.2)

Show the number of non-zero rows of each matrix of discriminant vectors

sapply(W, function(x) sum(rowSums(x) != 0))

Test semi supervised learning

Set certain class labels and subsets of views as missing

Z[90:100, ] = rep(NA, 3) X_list[[1]][1:10, ] = NA X_list[[2]][11:20, ] = NA W = jacaTrain(Z, X_list, kmax = 200, eps = 1e-06, lambda = rep(0.05, 2),alpha = 0.5, rho = 0.2, missing = TRUE)

Show the number of non-zero rows of each matrix of discriminant vectors

sapply(W, function(x) sum(rowSums(x) != 0))

Pennisetum/JACA documentation built on April 30, 2021, 12:30 a.m.