cvSLOPE: Tune SLOPE with Cross-Validation

In SLOPE: Sorted L1 Penalized Estimation

Tune SLOPE with Cross-Validation

Description

This function trains a model fit by SLOPE() by tuning its parameters through cross-validation.

Usage

cvSLOPE(
  x,
  y,
  q = 0.2,
  gamma = 0,
  n_folds = 10,
  n_repeats = 1,
  measure = c("mse", "mae", "deviance", "misclass", "auc"),
  refit = TRUE,
  ...
)

Arguments

x	the design matrix, which can be either a dense matrix of the standard matrix class, or a sparse matrix inheriting from Matrix::sparseMatrix. Data frames will be converted to matrices internally.
y	the response, which for family = "gaussian" must be numeric; for family = "binomial" or family = "multinomial", it can be a factor.
q	a vector of quantiles for the q parameter in SLOPE
gamma	relaxation parameter for SLOPE. Default is 0.0, which implies to relaxation of the penalty.
n_folds	number of folds (cross-validation)
n_repeats	number of folds (cross-validation)
measure	DEPRECATED
refit	logical; if TRUE, refits the model on the full dataset using the optimal parameters. Default is TRUE.
...	other arguments to pass on to SLOPE()

Value

An object of class "TrainedSLOPE", with the following slots:

summary	a summary of the results with means, standard errors, and 0.95 confidence levels
data	the raw data from the model training
optima	a data.frame of the best (mean) values for the different metrics and their corresponding parameter values
measure	a data.frame listing the used metric and its label
model	the model fit to the entire dataset using optimal parameters (only present if refit = TRUE)
call	the call

See Also

Other model-tuning: plot.TrainedSLOPE(), refit(), summary.TrainedSLOPE(), trainSLOPE()

Examples

# 8-fold cross-validation
tune <- cvSLOPE(
  subset(mtcars, select = c("mpg", "drat", "wt")),
  mtcars$hp,
  q = c(0.1, 0.2),
  n_folds = 8,
  n_repeats = 2,
  measure = "mse"
)

# Access the refitted model
tune$model

# Or use refit() to refit with different measure
fit <- refit(
  tune,
  subset(mtcars, select = c("mpg", "drat", "wt")),
  mtcars$hp
)
coef(fit)

SLOPE documentation built on Jan. 28, 2026, 9:07 a.m.