mlt: ctm – R documentation

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mlt

ctm

Conditional Transformation Models

Description

Specification of conditional transformation models

Usage

ctm(response, interacting = NULL, shifting = NULL, data = NULL, 
    todistr = c("Normal", "Logistic", "MinExtrVal", "MaxExtrVal", "Exponential"), 
    sumconstr = inherits(interacting, c("formula", "formula_basis")), ...)

Arguments

`response`	a basis function, ie, an object of class `basis`
`interacting`	a basis function, ie, an object of class `basis`
`shifting`	a basis function, ie, an object of class `basis`
`data`	either a `data.frame` containing the model variables or a formal description of these variables in an object of class `vars`
`todistr`	a character vector describing the distribution to be transformed
`sumconstr`	a logical indicating if sum constraints shall be applied
`...`	arguments to `as.basis` when `shifting` is a formula

Details

This function only specifies the model which can then be fitted using mlt. The shift term is positive by default.

Possible choices of the distributions the model transforms to (the inverse link functions) include the standard normal ("Normal"), the standard logistic ("Logistic"), the standard minimum extreme value ("MinExtrVal", also known as Gompertz distribution), and the standard maximum extreme value ("MaxExtrVal", also known as Gumbel distribution) distributions. The exponential distribution ("Exponential") can be used to fit Aalen additive hazard models.

Value

An object of class ctm.

References

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

mlt

Most Likely Transformations

v1.3-0

GPL-2

Authors

Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>)

Initial release

2021-03-03

ctm