inner_update: Updating index coefficients and non-linear functions...
In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

inner_update

R Documentation

Updating index coefficients and non-linear functions iteratively

Description

Iteratively updates index coefficients and non-linear functions using mixed integer programming. (A helper function used within update_smimodelFit; users are not expected to directly call this function.)

Usage

inner_update(
  x,
  data,
  yvar,
  family = gaussian(),
  index.vars,
  s.vars,
  linear.vars,
  num_ind,
  dgz,
  alpha_old,
  lambda0 = 1,
  lambda2 = 1,
  M = 10,
  max.iter = 50,
  tol = 0.001,
  TimeLimit = Inf,
  MIPGap = 1e-04,
  NonConvex = -1,
  verbose = list(solver = FALSE, progress = FALSE)
)

Arguments

`x`	Fitted `gam`.
`data`	Training data set on which models will be trained. Should be a `tsibble`.
`yvar`	Name of the response variable as a character string.
`family`	A description of the error distribution and link function to be used in the model (see `glm` and `family`).
`index.vars`	A `character` vector of names of the predictor variables for which indices should be estimated.
`s.vars`	A `character` vector of names of the predictor variables for which splines should be fitted individually (rather than considering as part of an index).
`linear.vars`	A `character` vector of names of the predictor variables that should be included linearly into the model.
`num_ind`	Number of indices.
`dgz`	The `tibble` of derivatives of the estimated smooths.
`alpha_old`	Current vector of index coefficients.
`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 loss.
`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.

Value

A list containing following elements:

`best_alpha`	The vector of best index coefficient estimates.
`min_loss`	Minimum value of the objective function(loss).
`index.ind`	An `integer` vector that assigns group index for each predictor, corresponding to `best_alpha`.
`ind_pos`	A list that indicates which predictors belong to which index, corresponding to `best_alpha`.
`X_new`	A matrix of selected predictor variables, corresponding to `best_alpha`.