vselect: Variable selection toolbox for lasso,

In WY-Chen/EqVarDAG: Causal Discovery with Equal Variance Assumption

Variable selection toolbox for lasso,

Description

Variable selection toolbox for lasso,

Usage

vselect(
  X,
  Y,
  alpha = 0.05,
  p_total = 0,
  selmtd = "dlasso",
  precmtd = "sqrtlasso",
  FCD = TRUE
)

Arguments

X, Y:	n x p and 1 x p matrix
alpha:	desired selection significance level
p_total:	total number of variables, used in bonferroni correction
selmtd:	variable selection method: avaiable methods: "dlasso": debiased lasso (default with FCD=True and precmtd="sqrtlasso"); "lasso": lasso with fixed lambda from [Penalized likelihood methods for estimation of sparse high-dimensional directed acyclic graphs. Shojaie and Michailidis. 2010]; "adalasso": adaptive lasso with fixed lambda from [Shojaie and Michailidis. 2010]; "cvlasso": cross-validated lasso from glmnet; "scallasso": scaled lasso.
FCD:	for debiased lasso, use the FCD procedure [False Discovery Rate Control via Debiased Lasso. Javanmard and Montanari. 2018] or use individual tests to select support.
precmtd:	for debiased lasso, how to compute debiasing matrix "cv": node-wise lasso w/ joint 10 fold cv "sqrtlasso": square-root lasso (no tune, default)

Value

one-hot model selection vector (1 x p) and fitted residuals

WY-Chen/EqVarDAG documentation built on May 10, 2022, 7:08 a.m.