absorber {absorber}

Variable selection in nonparametric models

Description

This function implements the method described in Savino, M. E. and Levy-Leduc, C (2024) for variable selection in nonlinear multivariate settings where observations are assumed to satisfy a nonparametric regression model. Each observation point should belong to [0,1]^p.

Usage

absorber(x, y, M = 3, K = 1, all.variables = NULL, parallel = FALSE, nbCore = 1)

Arguments

x

matrix of p columns containing the input values of the observations, each observation belonging to [0,1]^p.

y

vector containing the corresponding response variable associated to the input values \texttt{x}.

M

order of the B-spline basis used in the regression model. Default is 3 (quadratic B-splines).

K

number of evenly spaced knots to use in the B-spline basis. Default value is 1.

all.variables

list of characters or integers, labels of the variables. Default is \texttt{NULL}.

parallel

logical, if TRUE then a parallelized version of the code is used. Default is FALSE.

nbCore

numerical, number of cores used for parallelization, if parallel is set to TRUE.

Value

selec.var

list of vectors of the selected variables, one vector for each penalization parameter.

aic.var

vector of variables selected using AIC.

Examples

# --- Loading values of x --- #
data('x_obs')
# --- Loading values of the corresponding y --- #
data('y_obs')
x_trunc = x_obs[1:70,,drop=FALSE]
y_trunc = y_obs[1:70]

# --- Variable selection of f1 --- #
absorber(x=x_trunc, y=y_trunc, M = 3)

# --- Parallel computing --- #
absorber(x=x_trunc, y=y_trunc, M = 3, parallel = TRUE, nbCore = 2)