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Converting a fitted gam object to a smimodelFit object

Source: R/model_smimodel.R
make_smimodelFit.Rd

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.