badmf.sims.qdtoep: Quadratic Discriminant Toeplitz Simulation

In ebridge2/badmf: Bayesian Decision-Making Forests (BaD-MFs)

Description

A function for simulating data generalizing the Toeplitz setting, where each class has a different covariance matrix. This results in a Quadratic Discriminant.

Usage

badmf.sims.qdtoep(n, d, rotate = FALSE, priors = NULL, D1 = 10,
  b = 0.4, rho = 0.5)

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.
D1	the dimensionality for the non-equal covariance terms. Defaults to 10.
b	a scaling parameter for the means. Defaults to 0.4.
rho	the scaling of the covariance terms, should be < 1. Defaults to 0.5.

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 A simulation for the reversed random trunk experiment, in which the maximal covariant directions are the same as the directions with the maximal mean diffe[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 = "badmf")

Author(s)

Eric Bridgeford

Examples

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

ebridge2/badmf documentation built on June 4, 2019, 8:53 a.m.