mgc.sims.joint: Joint Normal Simulation

In mgc: Multiscale Graph Correlation

Description

A function for Generating a joint-normal simulation.

Usage

mgc.sims.joint(n, d, eps = 0.5)

Arguments

n	the number of samples for the simulation.
d	the number of dimensions for the simulation setting.
eps	the noise level for the simulation. Defaults to 0.5.

Value

a list containing the following:

X	[n, d] the data matrix with n samples in d dimensions.
Y	[n] the response array.

Details

Given: r = 1/2*d, Id is the identity matrix of size dxd, Jd is the matrix of ones of size dxd. Simulates n points from Joint-Normal(X, Y), where:

(X, Y) ~ N(0, E)

,

E = [Id, r*Jd; r*Jd, (1+eps*K)*Id]

and K = 1 if d=1, and 0 otherwise controls the noise for higher dimensions.

For more details see the help vignette: vignette("sims", package = "mgc")

Author(s)

Eric Bridgeford

Examples

library(mgc)
result  <- mgc.sims.joint(n=100, d=10)  # simulate 100 samples in 10 dimensions
X <- result$X; Y <- result$Y

mgc documentation built on July 1, 2020, 7:09 p.m.