PLR.wrap {LorenzRegression}

Wrapper for the Lorenz.SCADFABS and Lorenz.FABS functions

Description

PLR.wrap standardizes the covariates, run the penalized regression and spits out the path of parameter vectors.

Usage

PLR.wrap(
  YX_mat,
  standardize = TRUE,
  weights = NULL,
  penalty = c("SCAD", "LASSO"),
  h,
  eps = 0.005,
  ...
)

Arguments

YX_mat

a matrix with the first column corresponding to the response vector, the remaining ones being the explanatory variables.

standardize

Should the variables be standardized before the estimation process? Default value is TRUE.

weights

vector of sample weights. By default, each observation is given the same weight.

penalty

penalty used in the Penalized Lorenz Regression. Possible values are "SCAD" (default) or "LASSO".

h

bandwidth of the kernel, determining the smoothness of the approximation of the indicator function.

eps

Only used if penalty="SCAD" or penalty="LASSO". Step size in the FABS or SCADFABS algorithm. Default value is 0.005.

...

Additional parameters corresponding to arguments passed in Lorenz.SCADFABS or Lorenz.FABS depending on the argument chosen in penalty.

Value

A list with several components:

lambda

vector gathering the different values of the regularization parameter

theta

matrix where column i provides the normalized estimated parameter vector corresponding to value lambda[i] of the regularization parameter.

LR2

vector where element i provides the Lorenz-R^2 of the regression related to value lambda[i] of the regularization parameter.

Gi.expl

vector where element i provides the estimated explained Gini coefficient related to value lambda[i] of the regularization parameter.

See Also

Lorenz.SCADFABS, Lorenz.FABS

Examples

data(Data.Incomes)
YX_mat <- Data.Incomes[,-2]
PLR.wrap(YX_mat, h = nrow(Data.Incomes)^(-1/5.5), eps = 0.005)