is.lambda.feasible.LOO: Check the feasibility of a tuning parameter lambda for LOO...

In argminCS: Argmin Inference over a Discrete Candidate Set

Check the feasibility of a tuning parameter \lambda for LOO algorithm.

Description

Check the feasibility of a tuning parameter \lambda for LOO algorithm by examining whether its resulting \nabla_i K_j is less than a threshold value, i.e., the first order stability is likely achieved. For further details, we refer to the paper Zhang et al 2024.

Usage

is.lambda.feasible.LOO(
  lambda,
  scaled.difference.matrix,
  sample.mean = NULL,
  threshold = 0.08,
  n.pairs = 100,
  seed = NULL
)

Arguments

lambda	The real-valued tuning parameter for exponential weightings (the calculation of softmin).
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). If your experiment involves hypothesis testing over more than one dimension, pass sample.mean=colMeans(scaled.difference.matrix) to speed up computation.
threshold	A threshold value to examine if the first order stability is likely achieved; defaults to 0.08. As its value gets smaller, the first order stability tends to increase while power might decrease.
n.pairs	The number of (i,j) pairs for estimation; defaults to 100.
seed	(Optional) An integer-valued seed for subsampling.

Value

A boolean value indicating if the given \lambda likely gives the first order stability.

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