estimate_nuisance_parameter_lasso {HMC}

The function for nuisance parameter estimation in anchored_lasso_testing().

Description

The function for nuisance parameter estimation in anchored_lasso_testing().

Usage

estimate_nuisance_parameter_lasso(
  nuisance_sample_1,
  nuisance_sample_2,
  pca_method = "sparse_pca",
  mean_method = "lasso",
  num_latent_factor = 1,
  local_environment = local_environment,
  verbose = TRUE
)

Arguments

nuisance_sample_1

Group 1 sample. Each row is a subject and each column corresponds to a feature.

nuisance_sample_2

Group 2 sample. Each row is a subject and each column corresponds to a feature.

pca_method

Methods used to estimate principle component The default is "sparse_pca", using sparse PCA from package PMA. Other choices are "dense_pca"—the regular PCA; and "hard"— hard-thresholding PCA, which also induces sparsity.

mean_method

Methods used to estimate the discriminant direction. Default is logistic Lasso "lasso".

num_latent_factor

The principle component that lasso coefficient anchors at. The default is PC1 = 1.

local_environment

A environment for hyperparameters shared between folds.

verbose

Print information to the console. Default is TRUE.

Value

A list of estimated nuisance quantities.

estimate_leading_pc

Leading principle components

estimate_mean_1

Sample mean for group 1

estimate_mean_2

Sample mean for group 1

estimate_lasso_beta

Logistic Lasso regression coefficients.

estimate_projection_direction

Anchored projection direction. It is similar to PC1 when signal is weak but similar to estimate_optimal_direction when the signal is moderately large.

estimate_optimal_direction

Discriminant direction.