mgc.sims.linear: Linear Simulation

In neurodata/r-mgc: Multiscale Graph Correlation

Description

A function for Generating a linear simulation.

Usage

mgc.sims.linear(n, d, eps = 1, ind = FALSE, a = -1, b = 1)

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 1.
ind	whether to sample x and y independently. Defaults to FALSE.
a	the lower limit for the range of the data matrix. Defaults to -1.
b	the upper limit for the range of the data matrix. Defaults to 1.

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. Simulates n points from Linear(X, Y), where:

X ~ U(a, b)^d

Y = w^T X + κ ε

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

Author(s)

Eric Bridgeford

Examples

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

neurodata/r-mgc documentation built on March 12, 2021, 9:45 a.m.