Description Usage Arguments Value Note Author(s) References See Also Examples

Train and predict logitboost-based classification algorithm using multivariate isotonic regression (linear regression for no monotone features) as weak learners, based on the adjacent-categories logistic model (see Agresti (2010)). For full details on this algorithm, see Conde et al. (2020).

1 2 3 4 5 6 7 8 |

`formula` |
A formula of the form |

`data` |
Data frame from which variables specified in |

`xlearn` |
(Required if no formula is given as the principal argument.) A data frame or matrix containing the explanatory variables. |

`ylearn` |
(Required if no formula is given as the principal argument.) A numeric vector or factor with numeric levels specifying the class for each observation. |

`xtest` |
A data frame or matrix of cases to be classified, containing the features used in |

`mfinal` |
Maximum number of iterations of the algorithm. |

`monotone_constraints` |
Numerical vector consisting of 1, 0 and -1, its length equals the number of features in |

`prior` |
The prior probabilities of class membership. If unspecified, equal prior probabilities are used. If present, the probabilities must be specified in the order of the factor levels. |

`...` |
Arguments passed to or from other methods. |

A list containing the following components:

`call` |
The (matched) function call. |

`trainset` |
Matrix with the training set used (first columns) and the class for each observation (last column). |

`prior` |
Prior probabilities of class membership used. |

`apparent` |
Apparent error rate. |

`mfinal` |
Number of iterations of the algorithm. |

`loglikelihood` |
Log-likelihood. |

`posterior` |
Posterior probabilities of class membership for |

`class` |
Labels of the class with maximal probability for |

This function may be called using either a formula and data frame, or a data frame and grouping variable, or a matrix and grouping variable as the first two arguments. All other arguments are optional.

Classes must be identified, either in a column of `data`

or in the `ylearn`

vector, by natural numbers varying from 1 to the number of classes. The number of classes must be greater than 1.

If there are missing values in either `data`

, `xlearn`

or `ylearn`

, corresponding observations will be deleted.

David Conde

Agresti, A. (2010). Analysis of Ordinal Categorical Data, 2nd edition. John Wiley and Sons. New Jersey.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
data(motors)
table(motors$condition)
## 1 2 3 4
## 83 67 70 60
## Let us consider the first three variables as predictors
data <- motors[, 1:3]
grouping = motors$condition
##
## Lower values of the amplitudes are expected to be
## related to higher levels of damage severity, so
## we can consider the following monotone constraints
monotone_constraints = rep(-1, 3)
set.seed(7964)
values <- runif(dim(data)[1])
trainsubset <- values < 0.2
obj <- amilb(data[trainsubset, ], grouping[trainsubset],
data[-trainsubset, ], 100, monotone_constraints)
## Apparent error
obj$apparent
## 4.761905
## Error rate
100*mean(obj$class != grouping[-trainsubset])
## 15.41219
``` |

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