WPL1: p-Wasserstein Linear Projections With an L_1 Penalty

In WpProj: Linear p-Wasserstein Projections

p-Wasserstein Linear Projections With an L_1 Penalty

Description

p-Wasserstein Linear Projections With an L_1 Penalty

Usage

WPL1(
  X,
  Y = NULL,
  theta = NULL,
  power = 2,
  penalty = c("lasso", "ols", "mcp", "elastic.net", "selection.lasso", "scad", "mcp.net",
    "scad.net", "grp.lasso", "grp.lasso.net", "grp.mcp", "grp.scad", "grp.mcp.net",
    "grp.scad.net", "sparse.grp.lasso"),
  model.size = NULL,
  lambda = numeric(0),
  nlambda = 100L,
  lambda.min.ratio = 1e-04,
  gamma = 1,
  maxit = 500L,
  tol = 1e-07,
  ...
)

Arguments

X	matrix of covariates
Y	matrix of predictions
theta	optional parameter matrix for selection methods.
power	power of the Wasserstein distance
penalty	Form of penalty. One of "lasso", "ols", "mcp", "elastic.net","selection.lasso", "scad", "mcp.net", "scad.net", "grp.lasso", "grp.lasso.net", "grp.mcp","grp.scad", "grp.mcp.net", "grp.scad.net", "sparse.grp.lasso"
model.size	How many coefficients should final model have
lambda	penalty parameter
nlambda	number of lambdas to explore
lambda.min.ratio	minimum ratio of max to min lambda
gamma	Tuning parameter for SCAD and MCP methods
maxit	maximum iterations for optimization
tol	tolerance for convergence
...	arguments passed to other methods such as Wasserstein distance

Value

object of class WpProj

See Also

W1L1(), W2L1(), WInfL1

WpProj documentation built on May 29, 2024, 7:55 a.m.