Description Usage Arguments Author(s)

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.

1 2 | ```
discr.sims.linear(n, d, K, signal.scale = 1, signal.lshift = 1,
non.scale = 1, rotate = FALSE, class.equal = TRUE, ind = FALSE)
``` |

`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 |

`signal.lshift` |
the location shift for the signal dimension between the classes. Defaults to |

`non.scale` |
the scaling for the non-signal dimensions. Defaults to |

`rotate` |
whether to apply a random rotation. Defaults to |

`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 |

`ind` |
whether to sample x and y independently. Defaults to |

Eric Bridgeford

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