mlt: mltoptim – R documentation

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mlt

mltoptim

Control Optimisation

Description

Define optimisers and their control parameters

Usage

mltoptim(auglag = list(maxtry = 5, kkt2.check = FALSE), 
         spg = list(maxit = 10000, quiet = TRUE, checkGrad = FALSE), 
         nloptr = NULL, trace = FALSE)

Arguments

`auglag`	A list with control parameters for the `auglag` optimiser. `maxtry` is the number of times the algorithm is started on random starting values in case it failed with the precomputed ones.
`spg`	A list with control parameters for the `BBoptim` optimiser (calling `spg` internally).
`nloptr`	A list with control parameters for the `nloptr` optimiser. This is still experimental and thus switched off (defaulting to `NULL`).
`trace`	A logical switching trace reports by the optimisers off.

Details

This function sets-up functions to be called in mlt internally.

Value

A list of functions with arguments theta (starting values), f (log-likelihood), g (scores), ui and ci (linear inequality constraints). Adding further such functions is a way to add more optimisers to mlt. The first one in this list converging defines the resulting model.

Examples

### set-up linear transformation model for conditional
  ### distribution of dist given speed
  dist <- numeric_var("dist", support = c(2.0, 100), bounds = c(0, Inf))
  ctmm <- ctm(response = Bernstein_basis(dist, order = 4, ui = "increasing"),
              shifting = ~ speed, data = cars)

  ### use auglag with kkt2.check = TRUE => the numerically determined
  ### hessian is returned as "optim_hessian" slot
  op <- mltoptim(auglag = list(maxtry = 5, kkt2.check = TRUE))[1]
  mltm <- mlt(ctmm, data = cars, scale = FALSE, optim = op)

  ### compare analytical and numerical hessian
  all.equal(c(Hessian(mltm)), c(mltm$optim_hessian), tol = 1e-4)

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