mgc.sims.ubern: Uncorrelated Bernoulli Simulation

In JINJINT/aLDG: Compute and evaluate the bivariate dependence

Uncorrelated Bernoulli Simulation

Description

A function for Generating an uncorrelated bernoulli simulation.

Usage

mgc.sims.ubern(n, d, eps = 0.5, p = 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.
p	the bernoulli probability.

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: w[i] = 1/i is a weight-vector that scales with the dimensionality. Simumlates n points from Wshape(X, Y) where:

U ~ B(p)

X ~ B(p)^d + eps*N(0, I_d)

Y = (2*U-1)w^T*X + 0.5*eps*N(0, 1)

vignette("sims", package = "mgc")

Author(s)

Eric Bridgeford

Examples

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

JINJINT/aLDG documentation built on April 1, 2022, 6:23 p.m.