Nothing
R/group_sparse_lm_aliases.R
In quadrupen: Sparse and Group Sparse Linear Models
Defines functions
sparse_coop_lasso
sparse_group_l1linf
sparse_group_lasso
coop_lasso
group_l1linf
group_lasso
Documented in
coop_lasso
group_l1linf
group_lasso
sparse_coop_lasso
sparse_group_l1linf
sparse_group_lasso
#' @rdname group_sparse_lm
#' @export
group_lasso <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list(method = "quadra")) {
out <- group_sparse_lm(
x,
y,
group,
type = "l2",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = 0.0,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
group_l1linf <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "linf",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = 0.0,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
coop_lasso <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "coop",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = 0.0,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
sparse_group_lasso <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
alpha = 0.5,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "l2",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = alpha,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
sparse_group_l1linf <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
alpha = 0.5,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "linf",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = alpha,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
sparse_coop_lasso <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
alpha = 0.5,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "coop",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = alpha,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
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quadrupen documentation built on Sept. 18, 2026, 1:06 a.m.
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Defines functions sparse_coop_lasso sparse_group_l1linf sparse_group_lasso coop_lasso group_l1linf group_lasso
Documented in coop_lasso group_l1linf group_lasso sparse_coop_lasso sparse_group_l1linf sparse_group_lasso
#' @rdname group_sparse_lm
#' @export
group_lasso <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list(method = "quadra")) {
out <- group_sparse_lm(
x,
y,
group,
type = "l2",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = 0.0,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
group_l1linf <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "linf",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = 0.0,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
coop_lasso <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "coop",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = 0.0,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
sparse_group_lasso <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
alpha = 0.5,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "l2",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = alpha,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
sparse_group_l1linf <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
alpha = 0.5,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "linf",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = alpha,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
#' @rdname group_sparse_lm
#' @export
sparse_coop_lasso <-
function(x,
y,
group,
lambda1 = NULL,
lambda2 = 0.0,
alpha = 0.5,
weights = rep(1,nrow(x)),
penscale = sqrt(tabulate(group)),
intercept = TRUE,
normalize = TRUE,
nlambda1 = ifelse(is.null(lambda1),100,length(lambda1)),
minratio = 1e-2,
maxfeat = ncol(x),
beta0 = numeric(ncol(x)),
control = list()) {
out <- group_sparse_lm(
x,
y,
group,
type = "coop",
lambda1 = lambda1,
lambda2 = lambda2,
alpha = alpha,
weights = weights,
penscale = penscale,
struct = Matrix::Diagonal(ncol(x), 1),
intercept = intercept,
normalize = normalize,
refit = FALSE,
nlambda1 = nlambda1,
minratio = minratio,
maxfeat = maxfeat,
beta0 = beta0,
control = control)
out
}
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