A function to apply the quantile classifier that uses a different optimal quantile probability for each variable

1 | ```
quantilecldiff(train, test, cl, theta = NULL, cl.test = NULL)
``` |

`train ` |
A matrix of data (the training set) with observations in rows and variables in columns. It can be a matrix or a dataframe. |

`test ` |
A matrix of data (the test set) with observations in rows and variables in columns. It can be a matrix or a dataframe. |

`cl ` |
A vector of class labels for each sample of the training set. It can be factor or numerical. |

`theta ` |
A vector of quantile probabilities (optional) |

`cl.test ` |
If available, a vector of class labels for each sample of the test set (optional) |

`quantilecldiff`

carries out the quantile classifier by using a different optimal quantile probability for each variable selected in the training set.

A list with components

`thetas ` |
The vector of quantile probabilities |

`theta.choice ` |
The mean of optimal quantile probabilities |

`me.train ` |
Misclassification error for the best quantile probability in the training set |

`me.test ` |
Misclassification error for the best quantile probability in the test set (only if |

`cl.train ` |
Predicted classification in the training set |

`cl.test ` |
Predicted classification in the test set |

Christian Hennig, Cinzia Viroli

See Also `quantilecl`

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