discr.sims.linear: Discriminability Linear Simulation

In mgc: Multiscale Graph Correlation

Description

A function to simulate multi-class data with a linear class-mean trend. The signal dimension is the dimension carrying all of the between-class difference, and the non-signal dimensions are noise.

Usage

discr.sims.linear(
  n,
  d,
  K,
  signal.scale = 1,
  signal.lshift = 1,
  non.scale = 1,
  rotate = FALSE,
  class.equal = TRUE,
  ind = FALSE
)

Arguments

n	the number of samples.
d	the number of dimensions. The first dimension will be the signal dimension; the remainders noise.
K	the number of classes in the dataset.
signal.scale	the scaling for the signal dimension. Defaults to 1.
signal.lshift	the location shift for the signal dimension between the classes. Defaults to 1.
non.scale	the scaling for the non-signal dimensions. Defaults to 1.
rotate	whether to apply a random rotation. Defaults to TRUE.
class.equal	whether the number of samples/class should be equal, with each class having a prior of 1/K, or inequal, in which each class obtains a prior of k/sum(K) for k=1:K. Defaults to TRUE.
ind	whether to sample x and y independently. Defaults to FALSE.

Author(s)

Eric Bridgeford

mgc documentation built on July 1, 2020, 7:09 p.m.