Description Usage Arguments Details Value References Examples

Normal linear model with benefits

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 linear model with simulaneous estimation of regression coefficients and scale parameter(s). This function also allows for stratum-specific intercepts and variances as well as censoring and truncation in the response.

Note that the scale of the parameters is different from what is reported by
`lm`

; the discrepancies are explained 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.

An object of class `Lm`

, 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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