lambda.adaptive.LOO: Generate a scaled.difference.matrix-driven lambda for LOO...

In argminCS: Argmin Inference over a Discrete Candidate Set

Generate a scaled.difference.matrix-driven \lambda for LOO algorithm.

Description

Generate a scaled.difference.matrix-driven \lambda for LOO algorithm motivated by the derivation of the first order stability. For its precise definition, we refer to the paper Zhang et al 2024.

Usage

lambda.adaptive.LOO(
  scaled.difference.matrix,
  sample.mean = NULL,
  const = 2.5,
  seed = NULL
)

Arguments

scaled.difference.matrix	A n by (p-1) difference scaled.difference.matrix matrix after column-wise scaling (reference dimension - the rest); each of its row is a (p-1)-dimensional vector of differences.
sample.mean	The sample mean of the n samples in scaled.difference.matrix; defaults to NULL. It can be calculated via colMeans(scaled.difference.matrix).
const	A scaling constant for the scaled.difference.matrix driven \lambda; defaults to 2.5. As its value gets larger, the first order stability tends to increase while power might decrease.
seed	(Optional) If provided, used to seed for tie-breaking (for reproducibility).

Value

A scaled.difference.matrix-driven \lambda for LOO algorithm.

Examples

# Simulate data
set.seed(123)
r <- 4
n <- 200
mu <- (1:20)/20
cov <- diag(length(mu))
set.seed(108)
data <- MASS::mvrnorm(n, mu, cov)
sample.mean <- colMeans(data)
diff.mat <- get.difference.matrix(data, r)
sample.mean.r <- get.sample.mean.r(sample.mean, r)
lambda <- lambda.adaptive.LOO(diff.mat, sample.mean=sample.mean.r)

argminCS documentation built on Aug. 8, 2025, 7:51 p.m.