Lorenz.boot.combine {LorenzRegression} | R Documentation |
Lorenz.boot.combine
combine outputs of different instances of the Lorenz.boot
function.
Lorenz.boot.combine(boot_list)
boot_list |
list of objects, each element being the output of a call to the function |
An object of class c("LR_boot", "LR")
or c("PLR_boot", "PLR")
, depending on whether a non-penalized or penalized regression was fitted.
The method confint
is used on an object of class "LR_boot"
or "PLR_boot"
to obtain bootstrap inference on the model parameters.
For the non-penalized Lorenz regression, the returned object is a list containing the following components:
theta
The estimated vector of parameters. In the penalized case, it is a matrix where each row corresponds to a different selection method (e.g., BIC, bootstrap, cross-validation).
Gi.expl
The estimated explained Gini coefficient. In the penalized case, it is a vector, where each element corresponds to a different selection method.
LR2
The Lorenz-R^2
of the regression. In the penalized case, it is a vector, where each element corresponds to a different selection method.
boot_out
An object of class "boot"
containing the output of the bootstrap calculation.
For the penalized Lorenz regression, the returned object is a list containing the following components:
path
See Lorenz.Reg
for the original path. To this path is added the out-of-bag (OOB) 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_cv"
if cross-validation was performed and used as a selection method in the penalized case.
Heuchenne, C. and A. Jacquemain (2022). Inference for monotone single-index conditional means: A Lorenz regression approach. Computational Statistics & Data Analysis 167(C).
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.
# Continuing the Lorenz.Reg(.) example for the penalized regression:
boot_list <- list()
set.seed(123)
boot_list[[1]] <- Lorenz.boot(PLR, R = 15, data.orig = data, boot_out_only = TRUE)
set.seed(456)
boot_list[[2]] <- Lorenz.boot(PLR, R = 15, data.orig = data, boot_out_only = TRUE)
PLR_boot <- Lorenz.boot.combine(boot_list)
summary(PLR_boot)