Package 'MGL'

Module network inference

Description

Takes a high-dimensional data matrix, initial values of the module latent variables, and a penalty parameter, and returns the final assignment of the data points to the modules, the values of the module latent variables, and the conditional dependency network among the module latent variables.

Usage

MGL(data, L, lambda, printoutput = 0, maxiter = 100, threshold = 0.01)

Arguments

data	An nxp matrix which contains n samples from p variables, where typically p>>n
L	An nxk matrix which contains the initial latent variable values, a column for each module
lambda	A penalty parameter controlling the sparsity of the conditional dependency network among the modules
printoutput	1 if the user wants the output from each iteration to be displayed, 0 for silent run
maxiter	Maximum number of iterations to be performed
threshold	Threshold for convergence

Value

L	An nxk matrix which contains the final latent variable values, a column for each module
theta	A kxk symmetric positive-semidefinite matrix respresenting the conditional dependency network among the modules
Z	A p-vector containing values between 1 to k, representing the assignment of the p variables to k modules

Examples

## Not run: 
library(MGL)
n = 20 #sample size
p = 100 #variable size
k = 5 #module size
lambda = .01 #penalty parameter to induce sparsity
data = matrix(rnorm(n*p), ncol=p)
# to start with initial random module latent variables
L = matrix(rnorm(n*k), ncol=k)
MGL(data, L, lambda)
# to start with k-means cluster centroids as module latent variables
L = t(kmeans(t(data), k)$centers)
MGL(data, L, lambda)

## End(Not run)

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