| Type: | Package |
| Title: | Variable Selection in Nonparametric Models using B-Splines |
| Version: | 1.0 |
| Date: | 2024-02-22 |
| Author: | Mary E. Savino [aut, cre], Celine Levy-Leduc [ctb] |
| Maintainer: | Mary E. Savino <mary.savino@outlook.fr> |
| Description: | A variable selection method using B-Splines in multivariate nOnparametric Regression models Based on partial dErivatives Regularization (ABSORBER) implements a novel variable selection method in a nonlinear multivariate model using B-splines. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2024), https://hal.science/hal-04434820. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 3.5.0), Matrix, sparsegl, fda, parallel |
| Imports: | ggplot2, MASS, irlba |
| Suggests: | knitr, markdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2024-02-22 10:30:51 UTC; mary |
| Repository: | CRAN |
| Date/Publication: | 2024-02-23 18:50:08 UTC |
Variable Selection in Nonparametric Models using B-Splines
Description
absorber consists of two functions: "absorber.R" and "plot_selection.R". For further information on how to use these functions, we refer the reader to the vignette of the package.
Details
Two datasets are also provided within this package and used as examples of this manual and in the vignette.
Author(s)
Mary E. Savino
Maintainer: Mary E. Savino <mary.savino@outlook.fr>
References
Savino, M. E. and Lévy-Leduc, C. (2024) A novel variable selection method in nonlinear multivariate models using B-splines with an application to geoscience. <https://hal.science/hal-04434820>.
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 |
y |
vector containing the corresponding response variable associated to the input values |
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 |
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)
Visualization of the selected variables
Description
This function produces a histogram of the variable selection percentage for each variable on which f depends. It also displays the results obtained with the AIC.
Usage
plot_selection(object)
Arguments
object |
output obtained with |
Value
This function produces a ggplot2::ggplot() plot to visualize the variables selected with absorber().
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 --- #
res = absorber(x=x_trunc, y=y_trunc, M = 3)
plot_selection(res)
Observation matrix x of five variables
Description
An example of 700 observations for the variable selection of function f_1 (see Savino and Lévy-Leduc (2024) for more details) with five input variables.
Usage
data("x_obs")Format
Numeric matrix of 700 rows and 5 columns.
Values of the response variable of the noisy observation set of five input variables
Description
An example of noisy observations obtained by adding a Gaussian noise to f_1(x_i) associated to the input values contained in x_obs.rda. See Savino and Lévy-Leduc (2024) for the expression of f_1.
Usage
data("y_obs")Format
Numeric vector of 700 values.