Updating a smimodelFit — update_smimodelFit • smimodel

Optimises and updates a given smimodelFit.

Usage

update_smimodelFit(
  object,
  data,
  lambda0 = 1,
  lambda2 = 1,
  M = 10,
  max.iter = 50,
  tol = 0.001,
  tolCoefs = 0.001,
  TimeLimit = Inf,
  MIPGap = 1e-04,
  NonConvex = -1,
  verbose = list(solver = FALSE, progress = FALSE),
  ...
)

Arguments

object

A smimodelFit object.

data

Training data set on which models will be trained. Must be a data set of class tsibble.(Make sure there are no additional date or time related variables except for the index of the tsibble).

lambda0

Penalty parameter for L0 penalty.

lambda2

Penalty parameter for L2 penalty.

M

Big-M value to be used in MIP.

max.iter

Maximum number of MIP iterations performed to update index coefficients for a given model.

tol

Tolerance for the objective function value (loss) of MIP.

tolCoefs

Tolerance for coefficients.

TimeLimit

A limit for the total time (in seconds) expended in a single MIP iteration.

MIPGap

Relative MIP optimality gap.

NonConvex

The strategy for handling non-convex quadratic objectives or non-convex quadratic constraints in Gurobi solver.

verbose

A named list controlling verbosity options. Defaults to list(solver = FALSE, progress = FALSE).

solver: Logical. If TRUE, print detailed solver output.
progress: Logical. If TRUE, print optimisation algorithm progress messages.

...

Other arguments not currently used.

Value

A list of optimised model information. For descriptions of the list elements see make_smimodelFit).