update_smimodelFit: Updating a 'smimodelFit'

In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

Updating a smimodelFit

Description

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).

smimodel documentation built on April 8, 2026, 5:06 p.m.