lol.sims.mean_diff: Mean Difference Simulation

In neurodata/lol: Linear Optimal Low-Rank Projection

Description

A function for simulating data in which a difference in the means is present only in a subset of dimensions, and equal covariance.

Usage

lol.sims.mean_diff(
  n,
  d,
  rotate = FALSE,
  priors = NULL,
  K = 2,
  md = 1,
  subset = c(1),
  offdiag = 0,
  s = 1
)

Arguments

n	the number of samples of the simulated data.
d	the dimensionality of the simulated data.
rotate	whether to apply a random rotation to the mean and covariance. With random rotataion matrix Q, mu = Q*mu, and S = Q*S*Q. Defaults to FALSE.
priors	the priors for each class. If NULL, class priors are all equal. If not null, should be |priors| = K, a length K vector for K classes. Defaults to NULL.
K	the number of classes. Defaults to 2.
md	the magnitude of the difference in the means in the specified subset of dimensions. Ddefaults to 1.
subset	the dimensions to have a difference in the means. Defaults to only the first dimension. max(subset) < d. Defaults to c(1).
offdiag	the off-diagonal elements of the covariance matrix. Should be < 1. S_{ij} = offdiag if i != j, or 1 if i == j. Defaults to 0.
s	the scaling parameter of the covariance matrix. S_ij = scaling*1 if i == j, or scaling*offdiag if i != j. Defaults to 1.

Value

A list of class simulation with the following:

X	[n, d] the n data points in d dimensions as a matrix.
Y	[n] the n labels as an array.
mus	[d, K] the K class means in d dimensions.
Sigmas	[d, d, K] the K class covariance matrices in d dimensions.
priors	[K] the priors for each of the K classes.
simtype	The name of the simulation.
params	Any extraneous parameters the simulation was created with.

Details

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

Author(s)

Eric Bridgeford

Examples

library(lolR)
data <- lol.sims.mean_diff(n=200, d=30)  # 200 examples of 30 dimensions
X <- data$X; Y <- data$Y

neurodata/lol documentation built on March 3, 2021, 1:46 a.m.