PLR.CV {LorenzRegression} | R Documentation |
Cross-validation for penalized Lorenz regression
Description
PLR.CV
selects the grid and penalization parameters of the penalized Lorenz regression by cross-validation.
Usage
PLR.CV(object, k, data.orig, seed.CV = NULL, parallel = FALSE, ...)
Arguments
object |
An object with S3 class |
k |
An integer indicating the number of folds in the k-fold cross-validation |
data.orig |
A data frame corresponding to the original dataset, used in the |
seed.CV |
An optional seed that is used internally for the creation of the folds. Default is |
parallel |
Whether parallel computing should be used to distribute the cross-validation computations. Either a logical value determining whether parallel computing is used (TRUE) or not (FALSE, the default value). Or a numerical value determining the number of cores to use. |
... |
Additional parameters corresponding to arguments passed to the function |
Details
The parameter seed.CV
allows for local seed setting to control randomness in the generation of the folds.
The specified seed is applied to the respective part of the computation, and the seed is reverted to its previous state after the operation.
This ensures that the seed settings do not interfere with the global random state or other parts of the code.
Value
An object of class c("PLR_cv", "PLR")
. The object is a list containing the following components:
path
See the
Lorenz.Reg
function for the documentation of the original path. To this path is added the CV-score.lambda.idx
A vector indicating the index of the optimal lambda obtained by each selection method.
grid.idx
A vector indicating the index of the optimal grid parameter obtained by each selection method.
Note: The returned object may have additional classes such as "PLR_boot"
if bootstrap was performed.
References
Jacquemain, A., C. Heuchenne, and E. Pircalabelu (2024). A penalised bootstrap estimation procedure for the explained Gini coefficient. Electronic Journal of Statistics 18(1) 247-300.
See Also
Lorenz.Reg
, Lorenz.SCADFABS
, Lorenz.FABS
, Lorenz.boot
Examples
# Continuing the Lorenz.Reg(.) example:
PLR_CV <- PLR.CV(PLR, k = 5, data.orig = data, seed.CV = 123)
# The object now inherits from the class "PLR_CV".
# Hence the methods (also) display the results obtained by cross-validation.
print(PLR_CV)
summary(PLR_CV)
coef(PLR_CV, pars.idx = "CV")
predict(PLR_CV, pars.idx = "CV")
plot(PLR_CV)
plot(PLR_CV, type = "diagnostic") # Plot of the scores depending on the grid and penalty parameters