mlt: Most Likely Transformations
In mlt: Most Likely Transformations

Description Usage Arguments Details Value References Examples

View source: R/mlt.R

Likelihood-based model estimation in conditional transformation models

1 2	mlt(model, data, weights = NULL, offset = NULL, fixed = NULL, theta = NULL, pstart = NULL, scale = FALSE, dofit = TRUE, optim = mltoptim(), ...)

`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

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

An object of class mlt with corresponding methods.

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

 
  ### 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"))