overlapping_gl: Fit an Overlapping Group Lasso Model

In hierNest: Penalized Regression with Hierarchical Nested Parameterization Structure

Fit an Overlapping Group Lasso Model

Description

This function fits an overlapping group lasso model with hierarchical regularization, allowing both sparsity within groups and across groups.

Usage

overlapping_gl(
  x,
  y,
  group = NULL,
  family = c("gaussian", "binomial"),
  nlambda = 100,
  lambda.factor = ifelse(nobs < nvars, 0.01, 1e-04),
  lambda = NULL,
  pf_group = sqrt(bs),
  pf_sparse = rep(1, nvars),
  intercept = TRUE,
  asparse1 = 1,
  asparse2 = 0.05,
  standardize = TRUE,
  lower_bnd = -Inf,
  upper_bnd = Inf,
  weights = NULL,
  offset = NULL,
  warm = NULL,
  trace_it = 0,
  dfmax = as.integer(max(group)) + 1L,
  pmax = min(dfmax * 1.2, as.integer(max(group))),
  eps = 1e-08,
  maxit = 3e+06,
  cn,
  drgix,
  drgiy,
  cn_s,
  cn_e,
  random_asparse = FALSE
)

Arguments

x	A numeric matrix of predictor variables (no missing values allowed).
y	A numeric vector of response variable values.
group	An integer vector defining the group membership for each predictor. Default is NULL (each predictor forms its own group).
family	A character string specifying the model family. Options are "gaussian" (default) and "binomial".
nlambda	Number of lambda values. Default is 100.
lambda.factor	Factor determining minimum lambda as a fraction of maximum lambda. Default depends on dimensionality.
lambda	Numeric vector of lambda values. If provided, overrides 'nlambda' and 'lambda.factor'.
pf_group	Penalty factor for groups. Default is square root of group size.
pf_sparse	Penalty factor for individual predictors. Default is 1 for each predictor.
intercept	Logical; whether to include an intercept. Default is TRUE.
asparse1	Sparsity penalty factor controlling group-level sparsity. Default is 1.
asparse2	Sparsity penalty factor controlling within-group sparsity. Default is 0.05.
standardize	Logical; if TRUE, standardizes predictors before fitting. Default is TRUE.
lower_bnd	Numeric vector specifying lower bounds for coefficients. Default is -Inf.
upper_bnd	Numeric vector specifying upper bounds for coefficients. Default is Inf.
weights	Optional numeric vector of observation weights. Currently limited functionality.
offset	Optional numeric vector specifying a known component to be included in the linear predictor.
warm	Optional initial values for optimization.
trace_it	Integer indicating the verbosity level. Default is 0 (no output).
dfmax	Maximum number of groups allowed in the model. Default derived from groups.
pmax	Maximum number of predictors allowed in the model. Default derived from groups.
eps	Numeric convergence threshold for optimization. Default is 1e-08.
maxit	Maximum number of iterations for optimization. Default is 3e+06.
cn	Additional internal numeric parameter for optimization.
drgix, drgiy	Numeric vectors specifying indices for specific group and predictor structures.
cn_s, cn_e	Numeric vectors specifying starting and ending indices for substructures.
random_asparse	Logical; if TRUE, randomly selects sparsity parameters. Default is FALSE.

Value

An object of class 'sparsegl' containing:

call	The matched function call.
lambda	The lambda values used for fitting.
asparse1, asparse2	Sparsity parameters used.
nobs	Number of observations.
pf_group, pf_sparse	Penalty factors used.
coefficients	Estimated coefficients.

Additional components relevant to model diagnostics and fitting.

hierNest documentation built on March 24, 2026, 5:07 p.m.