# mlt: Most Likely Transformations In mlt: Most Likely Transformations

## Description

Likelihood-based model estimation in conditional transformation models

## Usage

 ```1 2``` ```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 weights `offset` an optional vector of offset values `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 `optim` a list of functions implementing suitable optimisers `...` additional arguments, currently ignored

## Details

This function fits a conditional transformation model by searching for the most likely transformation as described in Hothorn et al. (2017).

## Value

An object of class `mlt` with corresponding methods.

## References

Torsten Hothorn, Lisa Moest, Peter Buehlmann (2017), Most Likely Transformations, Scandinavian Journal of Statistics, Accepted 2017-06-19, http://arxiv.org/abs/1508.06749.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ``` ### 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 21, 2017, 1:03 a.m.