Description Usage Arguments Details Value References

Marginally interpretable transformation models for clustered data. Highly experimental, use at your own risk.

1 2 3 4 |

`object` |
A |

`formula` |
A formula specifying the random effects. |

`data` |
A data frame. |

`standardise` |
Two types of models can be estimated: M1 (with |

`grd` |
A sparse grid used for numerical integration to get the likelihood. |

`Hessian` |
A logical, if |

`...` |
Additional argument. |

A Gaussian copula with a correlation structure obtained from a random
intercept or random intercept / random slope model (that is, clustered or
longitudinal data can by modelled only) is used to capture the
correlations whereas the marginal distributions are described by a
transformation model. The methodology is described in Hothorn (2019)
and examples are given in the `mtram`

package vignette.

This is a proof-of-concept implementation and still highly experimental.
Only `coef()`

and `logLik()`

methods are available at the
moment.

An object of class `tram`

with `coef()`

and `logLik()`

methods.

Torsten Hothorn (2019). Marginally Interpretable Parametric Linear Transformation Models for Clustered Observations. Technical Report.

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