mlt: Most Likely Transformations

In mlt: Most Likely Transformations

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")}

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 Aug. 21, 2023, 5:06 p.m.