make_smimodelFit: Converting a fitted 'gam' object to a 'smimodelFit' object
In smimodel: Sparse Multiple Index Models for Nonparametric Forecasting

make_smimodelFit

R Documentation

Converting a fitted `gam` object to a `smimodelFit` object

Description

Converts a given object of class gam to an object of class smimodelFit.

Usage

make_smimodelFit(
  x,
  data,
  yvar,
  neighbour,
  index.vars,
  index.ind,
  index.data,
  index.names,
  alpha,
  s.vars = NULL,
  linear.vars = NULL,
  lambda0 = NULL,
  lambda2 = NULL,
  M = NULL,
  max.iter = NULL,
  tol = NULL,
  tolCoefs = NULL,
  TimeLimit = NULL,
  MIPGap = NULL,
  NonConvex = NULL
)

Arguments

`x`	A fitted `gam` object.
`data`	The original training data set.
`yvar`	Name of the response variable as a character string.
`neighbour`	`neighbour` argument passed from the outer function.
`index.vars`	A `character` vector of names of the predictor variables for which indices are estimated.
`index.ind`	An `integer` vector that assigns group index for each predictor in `index.vars`.
`index.data`	A `tibble` including columns for the constructed indices.
`index.names`	A `character` vector of names of the constructed indices.
`alpha`	A vector of index coefficients.
`s.vars`	A `character` vector of names of the predictor variables for which splines are fitted individually (rather than considering as part of an index).
`linear.vars`	A `character` vector of names of the predictor variables that are included linearly in the model.
`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.

Value

An object of class smimodelFit, which is a list that contains following elements:

`alpha`	A sparse matrix of index coefficients vectors. Each column of the matrix corresponds to the index coefficient vector of each index.
`derivatives`	A `tibble` of derivatives of the estimated smooths.
`var_y`	Name of the response variable.
`vars_index`	A `character` vector of names of the predictor variables for which indices are estimated.
`vars_s`	A `character` vector of names of the predictor variables for which splines are fitted individually.
`vars_linear`	A `character` vector of names of the predictor variables that are included linearly in the model.
`neighbour`	Number of neighbours of each key considered in model fitting.
`gam`	Fitted `gam`.
`lambda0`	L0 penalty parameter used for model fitting.
`lambda2`	L2 penalty parameter used for model fitting.
`M`	Big-M value used in MIP.
`max.iter`	Maximum number of MIP iterations for a single round of index coefficients update.
`tol`	Tolerance for the objective function value (loss) used in solving MIP.
`tolCoefs`	Tolerance for coefficients used in updating index coefficients.
`TimeLimit`	Limit for the total time (in seconds) expended in a single MIP iteration.
`MIPGap`	Relative MIP optimality gap used.
`Nonconvex`	The strategy used for handling non-convex quadratic objectives or non-convex quadratic constraints in Gurobi solver.