Package 'TRexSelector' reference manual

Title:	T-Rex Selector: High-Dimensional Variable Selection & FDR Control
Description:	Performs fast variable selection in high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level. The package is based on the paper Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>.
Authors:	Jasin Machkour [aut, cre], Simon Tien [aut], Daniel P. Palomar [aut], Michael Muma [aut]
Maintainer:	Jasin Machkour <[email protected]>
License:	GPL (>= 3)
Version:	1.0.0
Built:	2025-01-31 06:16:15 UTC
Source:	https://github.com/jasinmachkour/trexselector

Add dummy predictors to the original predictor matrix

Description

Sample num_dummies dummy predictors from the univariate standard normal distribution and append them to the predictor matrix X.

Usage

add_dummies(X, num_dummies)
add_dummies(X, num_dummies)

Arguments

`X`	Real valued predictor matrix.
`num_dummies`	Number of dummies that are appended to the predictor matrix.

Value

Enlarged predictor matrix.

Examples

set.seed(123)
n <- 50
p <- 100
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
add_dummies(X = X, num_dummies = p)
set.seed(123)
n <- 50
p <- 100
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
add_dummies(X = X, num_dummies = p)

Add dummy predictors to the original predictor matrix, as required by the T-Rex+GVS selector (doi:10.23919/EUSIPCO55093.2022.9909883)

Description

Generate num_dummies dummy predictors as required for the T-Rex+GVS selector (doi:10.23919/EUSIPCO55093.2022.9909883) and append them to the predictor matrix X.

Usage

add_dummies_GVS(X, num_dummies, corr_max = 0.5)
add_dummies_GVS(X, num_dummies, corr_max = 0.5)

Arguments

`X`	Real valued predictor matrix.
`num_dummies`	Number of dummies that are appended to the predictor matrix. Has to be a multiple of the number of original variables.
`corr_max`	Maximum allowed correlation between any two predictors from different clusters.

Value

Enlarged predictor matrix for the T-Rex+GVS selector.

Examples

set.seed(123)
n <- 50
p <- 100
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
add_dummies_GVS(X = X, num_dummies = p)
set.seed(123)
n <- 50
p <- 100
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
add_dummies_GVS(X = X, num_dummies = p)

False discovery proportion (FDP)

Description

Computes the FDP based on the estimated and the true regression coefficient vectors.

Usage

FDP(beta_hat, beta, eps = .Machine$double.eps)
FDP(beta_hat, beta, eps = .Machine$double.eps)

Arguments

`beta_hat`	Estimated regression coefficient vector.
`beta`	True regression coefficient vector.
`eps`	Numerical zero.

Value

False discovery proportion (FDP).

Examples

data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
beta <- Gauss_data$beta

set.seed(1234)
res <- trex(X, y)
beta_hat <- res$selected_var

FDP(beta_hat = beta_hat, beta = beta)
data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
beta <- Gauss_data$beta

set.seed(1234)
res <- trex(X, y)
beta_hat <- res$selected_var

FDP(beta_hat = beta_hat, beta = beta)

Computes the conservative FDP estimate of the T-Rex selector (doi:10.48550/arXiv.2110.06048)

Description

Computes the conservative FDP estimate of the T-Rex selector (doi:10.48550/arXiv.2110.06048)

Usage

fdp_hat(V, Phi, Phi_prime, eps = .Machine$double.eps)
fdp_hat(V, Phi, Phi_prime, eps = .Machine$double.eps)

Arguments

`V`	Voting level grid.
`Phi`	Vector of relative occurrences.
`Phi_prime`	Vector of deflated relative occurrences.
`eps`	Numerical zero.

Value

Vector of conservative FDP estimates for each value of the voting level grid.

Toy data generated from a Gaussian linear model

Description

A data set containing a predictor matrix X with n = 50 observations and p = 100 variables (predictors), and a sparse parameter vector beta with associated support vector.

Usage

Gauss_data
Gauss_data

Format

A list containing a matrix X and vectors y, beta, and support:

X: Predictor matrix, n = 50, p = 100.
y: Response vector.
beta: Parameter vector.
support: Support vector.

Examples

# Generated as follows:
set.seed(789)
n <- 50
p <- 100
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
beta <- c(rep(5, times = 3), rep(0, times = 97))
support <- beta > 0
y <- X %*% beta + stats::rnorm(n)
Gauss_data <- list(
  X = X,
  y = y,
  beta = beta,
  support = support
)
# Generated as follows:
set.seed(789)
n <- 50
p <- 100
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
beta <- c(rep(5, times = 3), rep(0, times = 97))
support <- beta > 0
y <- X %*% beta + stats::rnorm(n)
Gauss_data <- list(
  X = X,
  y = y,
  beta = beta,
  support = support
)

Perform one random experiment

Description

Run one random experiment of the T-Rex selector (doi:10.48550/arXiv.2110.06048), i.e., generates dummies, appends them to the predictor matrix, and runs the forward selection algorithm until it is terminated after T_stop dummies have been selected.

Usage

lm_dummy(
  X,
  y,
  model_tlars,
  T_stop = 1,
  num_dummies = ncol(X),
  method = "trex",
  GVS_type = "IEN",
  type = "lar",
  corr_max = 0.5,
  lambda_2_lars = NULL,
  early_stop = TRUE,
  verbose = TRUE,
  intercept = FALSE,
  standardize = TRUE
)
lm_dummy(
  X,
  y,
  model_tlars,
  T_stop = 1,
  num_dummies = ncol(X),
  method = "trex",
  GVS_type = "IEN",
  type = "lar",
  corr_max = 0.5,
  lambda_2_lars = NULL,
  early_stop = TRUE,
  verbose = TRUE,
  intercept = FALSE,
  standardize = TRUE
)

Arguments

`X`	Real valued predictor matrix.
`y`	Response vector.
`model_tlars`	Object of the class tlars_cpp. It contains all state variables of the previous T-LARS step (necessary for warm-starts, i.e., restarting the forward selection process exactly where it was previously terminated).
`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.
`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.
`verbose`	Logical. If TRUE progress in computations is shown when performing T-LARS steps on the created model.
`intercept`	Logical. If TRUE an intercept is included.
`standardize`	Logical. If TRUE the predictors are standardized and the response is centered.

Value

Object of the class tlars_cpp.

Examples

set.seed(123)
eps <- .Machine$double.eps
n <- 75
p <- 100
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
beta <- c(rep(3, times = 3), rep(0, times = 97))
y <- X %*% beta + rnorm(n)
res <- lm_dummy(X = X, y = y, T_stop = 1, num_dummies = 5 * p)
beta_hat <- res$get_beta()[seq(p)]
support <- abs(beta_hat) > eps
support
set.seed(123)
eps <- .Machine$double.eps
n <- 75
p <- 100
X <- matrix(stats::rnorm(n * p), nrow = n, ncol = p)
beta <- c(rep(3, times = 3), rep(0, times = 97))
y <- X %*% beta + rnorm(n)
res <- lm_dummy(X = X, y = y, T_stop = 1, num_dummies = 5 * p)
beta_hat <- res$get_beta()[seq(p)]
support <- abs(beta_hat) > eps
support

Computes the Deflated Relative Occurrences

Description

Computes the vector of deflated relative occurrences for all variables (i.e., j = 1,..., p) and T = T_stop.

Usage

Phi_prime_fun(
  p,
  T_stop,
  num_dummies,
  phi_T_mat,
  Phi,
  eps = .Machine$double.eps
)
Phi_prime_fun(
  p,
  T_stop,
  num_dummies,
  phi_T_mat,
  Phi,
  eps = .Machine$double.eps
)

Arguments

`p`	Number of candidate variables.
`T_stop`	Number of included dummies after which the random experiments (i.e., forward selection processes) are stopped.
`num_dummies`	Number of dummies
`phi_T_mat`	Matrix of relative occurrences for all variables (i.e., j = 1,..., p) and for T = 1, ..., T_stop.
`Phi`	Vector of relative occurrences for all variables (i.e., j = 1,..., p) at T = T_stop.
`eps`	Numerical zero.

Value

Vector of deflated relative occurrences for all variables (i.e., j = 1,..., p) and T = T_stop.

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$double.eps
)
random_experiments(
  X,
  y,
  K = 20,
  T_stop = 1,
  num_dummies = ncol(X),
  method = "trex",
  GVS_type = "EN",
  type = "lar",
  corr_max = 0.5,
  lambda_2_lars = NULL,
  early_stop = TRUE,
  lars_state_list,
  verbose = TRUE,
  intercept = FALSE,
  standardize = TRUE,
  dummy_coef = FALSE,
  parallel_process = FALSE,
  parallel_max_cores = min(K, max(1, parallel::detectCores(logical = FALSE))),
  seed = NULL,
  eps = .Machine$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$phi_T_mat
relative_occurrences_matrix
set.seed(123)
data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
res <- random_experiments(X = X, y = y)
relative_occurrences_matrix <- res$phi_T_mat
relative_occurrences_matrix

Run the Screen-T-Rex selector (doi:10.1109/SSP53291.2023.10207957)

Description

The Screen-T-Rex selector (doi:10.1109/SSP53291.2023.10207957) performs very fast variable selection in high-dimensional settings while informing the user about the automatically selected false discovery rate (FDR).

Usage

screen_trex(
  X,
  y,
  K = 20,
  R = 1000,
  method = "trex",
  bootstrap = FALSE,
  conf_level_grid = seq(0, 1, by = 0.001),
  cor_coef = NA,
  type = "lar",
  corr_max = 0.5,
  lambda_2_lars = NULL,
  rho_thr_DA = 0.02,
  parallel_process = FALSE,
  parallel_max_cores = min(K, max(1, parallel::detectCores(logical = FALSE))),
  seed = NULL,
  eps = .Machine$double.eps,
  verbose = TRUE
)
screen_trex(
  X,
  y,
  K = 20,
  R = 1000,
  method = "trex",
  bootstrap = FALSE,
  conf_level_grid = seq(0, 1, by = 0.001),
  cor_coef = NA,
  type = "lar",
  corr_max = 0.5,
  lambda_2_lars = NULL,
  rho_thr_DA = 0.02,
  parallel_process = FALSE,
  parallel_max_cores = min(K, max(1, parallel::detectCores(logical = FALSE))),
  seed = NULL,
  eps = .Machine$double.eps,
  verbose = TRUE
)

Arguments

`X`	Real valued predictor matrix.
`y`	Response vector.
`K`	Number of random experiments.
`R`	Number of bootstrap resamples.
`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.
`bootstrap`	Logical. If TRUE Screen-T-Rex is carried out with bootstrapping.
`conf_level_grid`	Confidence level grid for the bootstrap confidence intervals.
`cor_coef`	AR(1) autocorrelation coefficient for the T-Rex+DA+AR1 selector or equicorrelation coefficient for the T-Rex+DA+equi selector.
`type`	'lar' for 'LARS' and 'lasso' for Lasso.
`corr_max`	Maximum allowed correlation between any two predictors from different clusters.
`lambda_2_lars`	lambda_2-value for LARS-based Elastic Net.
`rho_thr_DA`	Correlation threshold for the T-Rex+DA+AR1 selector and the T-Rex+DA+equi selector (i.e., method = 'trex+DA+AR1' or 'trex+DA+equi').
`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.
`verbose`	Logical. If TRUE progress in computations is shown.

Value

A list containing the estimated support vector, the automatically selected false discovery rate (FDR) and additional information.

Examples

data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
set.seed(123)
res <- screen_trex(X = X, y = y)
selected_var <- res$selected_var
selected_var
data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
set.seed(123)
res <- screen_trex(X = X, y = y)
selected_var <- res$selected_var
selected_var

Compute set of selected variables

Description

Computes the set of selected variables and returns the estimated support vector for the T-Rex selector (doi:10.48550/arXiv.2110.06048).

Usage

select_var_fun(p, tFDR, T_stop, FDP_hat_mat, Phi_mat, V)
select_var_fun(p, tFDR, T_stop, FDP_hat_mat, Phi_mat, V)

Arguments

`p`	Number of candidate variables.
`tFDR`	Target FDR level (between 0 and 1, i.e., 0% and 100%).
`T_stop`	Number of included dummies after which the random experiments (i.e., forward selection processes) are stopped.
`FDP_hat_mat`	Matrix whose rows are the vectors of conservative FDP estimates for each value of the voting level grid.
`Phi_mat`	Matrix of relative occurrences as determined by the T-Rex calibration algorithm.
`V`	Voting level grid.

Value

Estimated support vector.

Compute set of selected variables for the T-Rex+DA+BT selector T-Rex+DA+BT selector (doi:10.48550/arXiv.2401.15796)

Description

Computes the set of selected variables and returns the estimated support vector for the T-Rex+DA+BT selector (doi:10.48550/arXiv.2401.15796).

Usage

select_var_fun_DA_BT(
  p,
  tFDR,
  T_stop,
  FDP_hat_array_BT,
  Phi_array_BT,
  V,
  rho_grid
)
select_var_fun_DA_BT(
  p,
  tFDR,
  T_stop,
  FDP_hat_array_BT,
  Phi_array_BT,
  V,
  rho_grid
)

Arguments

`p`	Number of candidate variables.
`tFDR`	Target FDR level (between 0 and 1, i.e., 0% and 100%).
`T_stop`	Number of included dummies after which the random experiments (i.e., forward selection processes) are stopped.
`FDP_hat_array_BT`	Array containing the conservative FDP estimates for all variables (dimension 1), values of the voting level grid (dimension 2), and values of the dendrogram grid (dimension 3).
`Phi_array_BT`	Array of relative occurrences as determined by the T-Rex calibration algorithm.
`V`	Voting level grid.
`rho_grid`	Dendrogram grid.

Value

List containing the estimated support vector, etc.

True positive proportion (TPP)

Description

Computes the TPP based on the estimated and the true regression coefficient vectors.

Usage

TPP(beta_hat, beta, eps = .Machine$double.eps)
TPP(beta_hat, beta, eps = .Machine$double.eps)

Arguments

`beta_hat`	Estimated regression coefficient vector.
`beta`	True regression coefficient vector.
`eps`	Numerical zero.

Value

True positive proportion (TPP).

Examples

data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
beta <- Gauss_data$beta

set.seed(1234)
res <- trex(X, y)
beta_hat <- res$selected_var

TPP(beta_hat = beta_hat, beta = beta)
data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
beta <- Gauss_data$beta

set.seed(1234)
res <- trex(X, y)
beta_hat <- res$selected_var

TPP(beta_hat = beta_hat, beta = beta)

Run the T-Rex selector (doi:10.48550/arXiv.2110.06048)

Description

The T-Rex selector (doi:10.48550/arXiv.2110.06048) performs fast variable selection in high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level.

Usage

trex(
  X,
  y,
  tFDR = 0.2,
  K = 20,
  max_num_dummies = 10,
  max_T_stop = TRUE,
  method = "trex",
  GVS_type = "IEN",
  cor_coef = NA,
  type = "lar",
  corr_max = 0.5,
  lambda_2_lars = NULL,
  rho_thr_DA = 0.02,
  hc_dist = "single",
  hc_grid_length = min(20, ncol(X)),
  parallel_process = FALSE,
  parallel_max_cores = min(K, max(1, parallel::detectCores(logical = FALSE))),
  seed = NULL,
  eps = .Machine$double.eps,
  verbose = TRUE
)
trex(
  X,
  y,
  tFDR = 0.2,
  K = 20,
  max_num_dummies = 10,
  max_T_stop = TRUE,
  method = "trex",
  GVS_type = "IEN",
  cor_coef = NA,
  type = "lar",
  corr_max = 0.5,
  lambda_2_lars = NULL,
  rho_thr_DA = 0.02,
  hc_dist = "single",
  hc_grid_length = min(20, ncol(X)),
  parallel_process = FALSE,
  parallel_max_cores = min(K, max(1, parallel::detectCores(logical = FALSE))),
  seed = NULL,
  eps = .Machine$double.eps,
  verbose = TRUE
)

Arguments

`X`	Real valued predictor matrix.
`y`	Response vector.
`tFDR`	Target FDR level (between 0 and 1, i.e., 0% and 100%).
`K`	Number of random experiments.
`max_num_dummies`	Integer factor determining the maximum number of dummies as a multiple of the number of original variables p (i.e., num_dummies = max_num_dummies * p).
`max_T_stop`	If TRUE the maximum number of dummies that can be included before stopping is set to ceiling(n / 2), where n is the number of data points/observations.
`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).
`cor_coef`	AR(1) autocorrelation coefficient for the T-Rex+DA+AR1 selector or equicorrelation coefficient for the T-Rex+DA+equi selector.
`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.
`rho_thr_DA`	Correlation threshold for the T-Rex+DA+AR1 selector and the T-Rex+DA+equi selector (i.e., method = 'trex+DA+AR1' or 'trex+DA+equi').
`hc_dist`	Distance measure of the hierarchical clustering/dendrogram (only for trex+DA+BT): 'single' for single-linkage, "complete" for complete linkage, "average" for average linkage (see hclust for more options).
`hc_grid_length`	Length of the height-cutoff-grid for the dendrogram (integer between 1 and the number of original variables p).
`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.
`verbose`	Logical. If TRUE progress in computations is shown.

Value

A list containing the estimated support vector and additional information, including the number of used dummies and the number of included dummies before stopping.

Examples

data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
set.seed(1234)
res <- trex(X = X, y = y)
selected_var <- res$selected_var
selected_var
data("Gauss_data")
X <- Gauss_data$X
y <- c(Gauss_data$y)
set.seed(1234)
res <- trex(X = X, y = y)
selected_var <- res$selected_var
selected_var

Package 'TRexSelector'

Help Index

Add dummy predictors to the original predictor matrix

Description

Usage

Arguments

Value

Examples

Add dummy predictors to the original predictor matrix, as required by the T-Rex+GVS selector (doi:10.23919/EUSIPCO55093.2022.9909883)

Description

Usage

Arguments

Value

Examples

False discovery proportion (FDP)

Description

Usage

Arguments

Value

Examples

Computes the conservative FDP estimate of the T-Rex selector (doi:10.48550/arXiv.2110.06048)

Description

Usage

Arguments

Value

Toy data generated from a Gaussian linear model

Description

Usage

Format

Examples

Perform one random experiment

Description

Usage

Arguments

Value

Examples

Computes the Deflated Relative Occurrences

Description

Usage

Arguments

Value

Run K random experiments

Description

Usage

Arguments

Value

Examples

Run the Screen-T-Rex selector (doi:10.1109/SSP53291.2023.10207957)

Description

Usage

Arguments

Value

Examples

Compute set of selected variables

Description

Usage

Arguments

Value

Compute set of selected variables for the T-Rex+DA+BT selector T-Rex+DA+BT selector (doi:10.48550/arXiv.2401.15796)

Description

Usage

Arguments

Value

True positive proportion (TPP)

Description

Usage

Arguments

Value

Examples

Run the T-Rex selector (doi:10.48550/arXiv.2110.06048)

Description

Usage

Arguments

Value

Examples