is.lambda.feasible.LOO {argminCS} | R Documentation |
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 |
seed |
(Optional) An integer-valued seed for subsampling. |
Value
A boolean value indicating if the given \lambda
likely gives the first order stability.