mltoptim {mlt}

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 = list(algorithm = "NLOPT_LD_MMA", xtol_rel = 1.0e-8), 
         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 family of optimisers.

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)