random_experiments {TRexSelector}

Run K random experiments

Description

Run K early terminated T-Rex (doi:10.48550/arXiv.2110.06048) random experiments and compute the matrix of relative occurrences for all variables and all numbers of included variables before stopping.

Usage

random_experiments(
  X,
  y,
  K = 20,
  T_stop = 1,
  num_dummies = ncol(X),
  method = "trex",
  GVS_type = "EN",
  type = "lar",
  corr_max = 0.5,
  lambda_2_lars = NULL,
  early_stop = TRUE,
  lars_state_list,
  verbose = TRUE,
  intercept = FALSE,
  standardize = TRUE,
  dummy_coef = FALSE,
  parallel_process = FALSE,
  parallel_max_cores = min(K, max(1, parallel::detectCores(logical = FALSE))),
  seed = NULL,
  eps = .Machine$double.eps
)

Arguments

X

Real valued predictor matrix.

y

Response vector.

K

Number of random experiments.

T_stop

Number of included dummies after which the random experiments (i.e., forward selection processes) are stopped.

num_dummies

Number of dummies that are appended to the predictor matrix.

method

'trex' for the T-Rex selector (doi:10.48550/arXiv.2110.06048), 'trex+GVS' for the T-Rex+GVS selector (doi:10.23919/EUSIPCO55093.2022.9909883), 'trex+DA+AR1' for the T-Rex+DA+AR1 selector, 'trex+DA+equi' for the T-Rex+DA+equi selector, 'trex+DA+BT' for the T-Rex+DA+BT selector (doi:10.48550/arXiv.2401.15796), 'trex+DA+NN' for the T-Rex+DA+NN selector (doi:10.48550/arXiv.2401.15139).

GVS_type

'IEN' for the Informed Elastic Net (doi:10.1109/CAMSAP58249.2023.10403489), 'EN' for the ordinary Elastic Net (doi:10.1111/j.1467-9868.2005.00503.x).

type

'lar' for 'LARS' and 'lasso' for Lasso.

corr_max

Maximum allowed correlation between any two predictors from different clusters (for method = 'trex+GVS').

lambda_2_lars

lambda_2-value for LARS-based Elastic Net.

early_stop

Logical. If TRUE, then the forward selection process is stopped after T_stop dummies have been included. Otherwise the entire solution path is computed.

lars_state_list

If parallel_process = TRUE: List of state variables of the previous T-LARS steps of the K random experiments (necessary for warm-starts, i.e., restarting the forward selection process exactly where it was previously terminated). If parallel_process = FALSE: List of objects of the class tlars_cpp associated with the K random experiments (necessary for warm-starts, i.e., restarting the forward selection process exactly where it was previously terminated).

verbose

Logical. If TRUE progress in computations is shown.

intercept

Logical. If TRUE an intercept is included.

standardize

Logical. If TRUE the predictors are standardized and the response is centered.

dummy_coef

Logical. If TRUE a matrix containing the terminal dummy coefficient vectors of all K random experiments as rows is returned.

parallel_process

Logical. If TRUE random experiments are executed in parallel.

parallel_max_cores

Maximum number of cores to be used for parallel processing.

seed

Seed for random number generator (ignored if parallel_process = FALSE).

eps

Numerical zero.

Value

List containing the results of the K random experiments.

Examples

set.seed(123)
data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
res <- random_experiments(X = X, y = y)
relative_occurrences_matrix <- res$phi_T_mat
relative_occurrences_matrix