mlt: Most Likely Transformations
In mlt: Most Likely Transformations

mlt	R Documentation

Most Likely Transformations

Description

Likelihood-based model estimation in conditional transformation models

Usage

mlt(model, data, weights = NULL, offset = NULL, fixed = NULL, theta = NULL, 
    pstart = NULL, scale = FALSE, dofit = TRUE, optim = mltoptim())

Arguments

`model`	a conditional transformation model as specified by `ctm`
`data`	a `data.frame` containing all variables specified in `model`
`weights`	an optional vector of case weights
`offset`	an optional vector of offset values; offsets are not added to an optional `scaling` term (see `link{ctm}`)
`fixed`	a named vector of fixed regression coefficients; the names need to correspond to column names of the design matrix
`theta`	optional starting values for the model parameters
`pstart`	optional starting values for the distribution function evaluated at the data
`scale`	a logical indicating if (internal) scaling shall be applied to the model coefficients
`dofit`	a logical indicating if the model shall be fitted to the data (`TRUE`) or not. If `theta` is given, a model of class `mlt` (a full "fitted" model) featuring these parameters is returned. Otherwise, an unfitted model of class `ctm` is returned
`optim`	a list of functions implementing suitable optimisers

Details

This function fits a conditional transformation model by searching for the most likely transformation as described in Hothorn et al. (2018) and Hothorn (2020).

Value

An object of class mlt with corresponding methods.

References

Torsten Hothorn, Lisa Moest, Peter Buehlmann (2018), Most Likely Transformations, Scandinavian Journal of Statistics, 45(1), 110–134, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/sjos.12291")}.

Torsten Hothorn (2020), Most Likely Transformations: The mlt Package, Journal of Statistical Software, 92(1), 1–68, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v092.i01")}

Sandra Siegfried, Lucas Kook, Torsten Hothorn (2023), Distribution-Free Location-Scale Regression, The American Statistician, 77(4), 345–356, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/00031305.2023.2203177")}.

Examples

 
  ### set-up conditional transformation model for conditional
  ### distribution of dist given speed
  dist <- numeric_var("dist", support = c(2.0, 100), bounds = c(0, Inf))
  speed <- numeric_var("speed", support = c(5.0, 23), bounds = c(0, Inf)) 
  ctmm <- ctm(response = Bernstein_basis(dist, order = 4, ui = "increasing"),
              interacting = Bernstein_basis(speed, order = 3))

  ### fit model
  mltm <- mlt(ctmm, data = cars)

  ### plot data
  plot(cars)
  ### predict quantiles and overlay data with model via a "quantile sheet"
  q <- predict(mltm, newdata = data.frame(speed = 0:24), type = "quantile", 
               p = 2:8 / 10, K = 500)
  tmp <- apply(q, 1, function(x) lines(0:24, x, type = "l"))

mlt documentation built on June 18, 2025, 1:08 a.m.