discr.sims.linear: Discriminability Linear Simulation

Description Usage Arguments Author(s)

View source: R/simulations.R

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

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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


neurodata/r-mgc documentation built on Dec. 25, 2019, 4:13 p.m.