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Updating index coefficients and non-linear functions iteratively

Source: R/model_smimodel.R
inner_update.Rd

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.