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.
is.lambda.feasible.LOO(
lambda,
scaled.difference.matrix,
sample.mean = NULL,
threshold = 0.08,
n.pairs = 100,
seed = NULL
)A boolean value indicating if the given \(\lambda\) likely gives the first order stability.
The real-valued tuning parameter for exponential weightings (the calculation of softmin).
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.
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.
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.
The number of \((i,j)\) pairs for estimation; defaults to 100.
(Optional) An integer-valued seed for subsampling.