grpnet {adelie}

Solves group elastic net via naive method.

Description

Solves group elastic net via naive method.

Usage

grpnet(
  X,
  glm,
  constraints = NULL,
  groups = NULL,
  alpha = 1,
  penalty = NULL,
  offsets = NULL,
  lmda_path = NULL,
  irls_max_iters = as.integer(10000),
  irls_tol = 1e-07,
  max_iters = as.integer(1e+05),
  tol = 1e-07,
  adev_tol = 0.9,
  ddev_tol = 0,
  newton_tol = 1e-12,
  newton_max_iters = 1000,
  n_threads = 1,
  early_exit = TRUE,
  intercept = TRUE,
  screen_rule = "pivot",
  min_ratio = 0.01,
  lmda_path_size = 100,
  max_screen_size = NULL,
  max_active_size = NULL,
  pivot_subset_ratio = 0.1,
  pivot_subset_min = 1,
  pivot_slack_ratio = 1.25,
  check_state = FALSE,
  progress_bar = TRUE,
  warm_start = NULL
)

Arguments

X

Feature matrix.

glm

GLM object.

constraints

Constraints.

groups

Groups.

alpha

Elastic net parameter.

penalty

Penalty factor.

offsets

Offsets.

lmda_path

The regularization path.

irls_max_iters

Maximum number of IRLS iterations.

irls_tol

IRLS convergence tolerance.

max_iters

Maximum number of coordinate descents.

tol

Coordinate descent convergence tolerance.

adev_tol

Percent deviance explained tolerance.

ddev_tol

Difference in percent deviance explained tolerance.

newton_tol

Convergence tolerance for the BCD update.

newton_max_iters

Maximum number of iterations for the BCD update.

n_threads

Number of threads.

early_exit

TRUE if the function should early exit.

intercept

TRUE to fit with intercept.

screen_rule

Screen rule.

min_ratio

Ratio between largest and smallest regularization.

lmda_path_size

Number of regularizations.

max_screen_size

Maximum number of screen groups.

max_active_size

Maximum number of active groups.

pivot_subset_ratio

Subset ratio of pivot rule.

pivot_subset_min

Minimum subset of pivot rule.

pivot_slack_ratio

Slack ratio of pivot rule.

check_state

Check state.

progress_bar

Progress bar.

warm_start

Warm start.

Value

State of the solver.

Examples

set.seed(0)
n <- 100
p <- 200
X <- matrix(rnorm(n * p), n, p)
y <- X[,1] * rnorm(1) + rnorm(n)
state <- grpnet(X, glm.gaussian(y))