W2L1: 2-Wasserstein distance linear projections with an L_1 penalty

In WpProj: Linear p-Wasserstein Projections

2-Wasserstein distance linear projections with an L_1 penalty

Description

2-Wasserstein distance linear projections with an L_1 penalty

Usage

W2L1(
  X,
  Y = NULL,
  theta = NULL,
  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"),
  method = c("projection", "selection.variable", "location.scale", "scale"),
  transport.method = transport_options(),
  epsilon = 0.05,
  OTmaxit = 100,
  model.size = NULL,
  lambda = numeric(0),
  nlambda = 100L,
  lambda.min.ratio = NULL,
  alpha = 1,
  gamma = 1,
  tau = 0.5,
  groups = numeric(0),
  scale.factor = numeric(0),
  penalty.factor = NULL,
  group.weights = NULL,
  maxit = 500L,
  tol = 1e-07,
  irls.maxit = 100L,
  irls.tol = 0.001,
  infimum.maxit = NULL,
  display.progress = FALSE
)

Arguments

X	An n x p matrix of covariates
Y	An n x s matrix of predictions
theta	optional parameter matrix for selection methods. Should be p x s.
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"
method	"selection.variable" or "projection
transport.method	Method for calculating the Wasserstein distance. One of "exact", "sinkhorn", "greenkhorn","hilbert"
epsilon	Penalty parameter for Sinkhorn and Greenkhorn and optimal transport
OTmaxit	Maximum iterations for the optimal transport iterations
model.size	The maximum number of desired covariates. Defaults to the number of covariates.
lambda	Penalty parameter for lasso regression. See oem.
nlambda	Number of lambda values. See oem.
lambda.min.ratio	Minimum lambda ratio for self selected lambda. See oem.
alpha	elastic net mixing. See oem.
gamma	tuning parameters for SCAD and MCP. See oem.
tau	mixing parameter for sparse group lasso. See oem.
groups	A vector of grouping values. See oem.
scale.factor	Value to standardize the covariates by. Typically, is the standard deviation. Should have length 1 or length same as the number of covariates
penalty.factor	Penalty factor for OEM. See oem.
group.weights	Weights for group lasso. See oem.
maxit	Max iteration for OEM. See oem.
tol	Tolerance for OEM. See oem.
irls.maxit	IRLS max iterations for OEM. See oem.
irls.tol	IRLS tolerance for OEM. See oem.
infimum.maxit	Maximum number of iterations alternating optimization and Wasserstein distance calculation. Irrelevant for projection method.
display.progress	Display intermediate progress?

Value

Object of class WpProj

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