Description Usage Arguments Details Value Author(s) References See Also

Prediction of class membership and posterior probabilities in local models using pairwise variable selection.

1 2 |

`object` |
an object of class ‘ |

`newdata` |
a data frame or matrix containing new data. If not given the same datas as used for training the ‘ |

`quick` |
indicator (logical), whether a quick, but less accurate computation of posterior probabalities should be used or not. |

`return.subclass.prediction` |
indicator (logical), whether the returned object includes posterior probabilities for each date in each subclass |

`...` |
Further arguments are passed to underlying |

Posterior probabilities are predicted as if object is a standard ‘`pvs`

’-model with the subclasses as classes. Then the posterior probabalities are summed over all subclasses for each class. The class with the highest value becomes the prediction.

If “`quick=FALSE`

” the posterior probabilites for each case are computed using the pairwise coupling algorithm presented by Hastie, Tibshirani (1998). If “`quick=FALSE`

” a much quicker solution is used, which leads to less accurate posterior probabalities. In almost all cases it doesn't has a negative effect on the classification result.

a list with components:

`class` |
the predicted (upper) classes |

`posterior` |
posterior probabilities for the (upper) classes |

`subclass.posteriors` |
(only if “ |

Gero Szepannek, [email protected], Christian Neumann

Szepannek, G. and Weihs, C. (2006) Local Modelling in Classification on Different Feature Subspaces.
In *Advances in Data Mining.*, ed Perner, P., LNAI 4065, pp. 226-234. Springer, Heidelberg.

`locpvs`

for learning ‘`locpvs`

’-models and examples for applying this predict method, `pvs`

for pairwise variable selection without modeling subclasses, `predict.pvs`

for predicting ‘`pvs`

’-models

klaR documentation built on March 19, 2018, 5:03 p.m.

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