cvSLOPE: Tune SLOPE with cross-validation

In jolars/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"),
  ...
)

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
...	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
call	the call

See Also

plot.TrainedSLOPE()

Other model-tuning: plot.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"
)

jolars/SLOPE documentation built on June 15, 2025, 1:45 p.m.