Description Usage Arguments Details Value References Examples

Non-normal linear regression inspired by Box-Cox models

1 |

`formula` |
an object of class |

`data` |
an optional data frame, list or environment (or object
coercible by |

`subset` |
an optional vector specifying a subset of observations to be used in the fitting process. |

`weights` |
an optional vector of weights to be used in the fitting
process. Should be |

`offset` |
this can be used to specify an _a priori_ known component to
be included in the linear predictor during fitting. This
should be |

`cluster` |
optional factor with a cluster ID employed for computing clustered covariances. |

`na.action` |
a function which indicates what should happen when the data
contain |

`...` |
additional arguments to |

A normal model for transformed responses, where the transformation is estimated from the data simultaneously with the regression coefficients. This is similar to a Box-Cox transformation, but the technical details differ. Examples can be found in the package vignette.

The model is defined with a negative shift term. Large values of the linear predictor correspond to large values of the conditional expectation response (but this relationship is potentially nonlinear).

An object of class `BoxCox`

, with corresponding `coef`

,
`vcov`

, `logLik`

, `estfun`

, `summary`

,
`print`

, `plot`

and `predict`

methods.

Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely
Transformations, *Scandinavian Journal of Statistics*, **45**(1),
110–134, doi: 10.1111/sjos.12291.

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