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

Calculates quartiles and random numbers according to the conditional distribution of residuals for the latent variable of a binary or ordinal regression, given the observed response value. See Details for an explanation.

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`object` |
the result of |

`...` |
unused |

For binary and ordinal regression, the regression models can be described by introducing a latent response variable Z of which the observed response Y is a classified version, and for which a linear regression applies. The errors of this "latent regression" have a logistic distribution. Given the linearly predicted value eta[i], which is the fitted value for the latent variable, the residual for Z[i] can therefore be assumed to have a logistic distribution.

This function calculates quantiles and random numbers according to the conditional distribution of residuals for Z[i], given the observed y[i].

a data.frame with the variables

`median` |
medians of the conditional distributions |

`lowq` |
lower quartiles |

`uppq` |
upper quartiles |

`random` |
random numbers, drawn according to the conditional distributions |

`fit` |
linear predictor values |

`y` |
observed response values |

Werner A. Stahel, ETH Zurich

See http://stat.ethz.ch/~stahel/regression

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