PLR.fit {LorenzRegression} | R Documentation |
Penalized Lorenz Regression Fit Function
Description
PLR.fit
fits a penalized Lorenz regression model using either the LASSO or SCAD penalty.
It serves as an internal wrapper that applies the fit function over a grid of tuning parameter values.
Usage
PLR.fit(y, x, weights = NULL, penalty, grid.arg, grid.value, lambda.list, ...)
Arguments
y |
A numeric vector representing the response variable. |
x |
A numeric matrix of covariates. |
weights |
An optional numeric vector of sample weights. Default is |
penalty |
A character string specifying the penalty type. Possible values are |
grid.arg |
A character string specifying the tuning parameter for which a grid is constructed. |
grid.value |
A numeric vector specifying the grid values for |
lambda.list |
An optional list specifying penalty values ( |
... |
Additional arguments passed to |
Details
The function applies either Lorenz.FABS
(for LASSO) or Lorenz.SCADFABS
(for SCAD) for each grid value.
The best model is selected based on the BIC score.
Value
A list containing:
path
A list of matrices, where each element corresponds to a grid value. Each matrix contains lambda values, Lorenz-
R^2
, explained Gini coefficients, BIC scores, and estimated coefficients.grid.idx
The index of the optimal grid parameter selected by the BIC criterion.
lambda.idx
The index of the optimal
\lambda
selected by the BIC criterion.grid.value
The grid values used for
grid.arg
.lambda.list
A list of
\lambda
values along the solution paths.grid.arg
The tuning parameter for which the grid was constructed.
See Also
Lorenz.FABS
, Lorenz.SCADFABS
, Lorenz.boot
, Lorenz.Reg
Examples
data(Data.Incomes)
y <- Data.Incomes$Income
x <- as.matrix(Data.Incomes[,-c(1,2)])
PLR.fit(y, x, penalty = "SCAD", grid.arg = "eps", grid.value = c(0.2,0.5), lambda.list = NULL)