badmf.sims.rtrunk: Random Trunk

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

Description

A simulation for the random trunk experiment, in which the maximal covariant dimensions are the reverse of the maximal mean differences.

Usage

badmf.sims.rtrunk(n, d, rotate = FALSE, priors = NULL, b = 4,
  K = 2, maxvar = 100)

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.
b	scalar for mu scaling. Default to 4.
K	number of classes, should be <4. Defaults to 2.
maxvar	the maximum covariance between the two classes. Defaults to 100.
maxvar.outlier	the maximum covariance for the outlier points. Defaults to maxvar*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] the priors for each of the K classes.
simtype	The name of the simulation.
params	Any extraneous parameters the simulation was created with.
robust	If robust is not false, a list containing inlier a boolean array indicating which points are inliers, s.outlier the covariance structure of outliers, and mu.outlier the means of the outliers.

Details

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

Author(s)

Eric Bridgeford

Examples

library(badmf)
data <- badmf.sims.rtrunk(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.