plot.TrainedSLOPE: Plot results from cross-validation

In jolars/SLOPE: Sorted L1 Penalized Estimation

Plot results from cross-validation

Description

Plot results from cross-validation

Usage

## S3 method for class 'TrainedSLOPE'
plot(
  x,
  plot_min = TRUE,
  ci_alpha = 0.2,
  ci_border = NA,
  ci_col = "salmon",
  plot_args = list(),
  polygon_args = list(),
  lines_args = list(),
  abline_args = list(),
  index = NULL,
  measure,
  ...
)

Arguments

x	an object of class 'TrainedSLOPE', typically from a call to trainSLOPE()
plot_min	whether to mark the location of the penalty corresponding to the best prediction score
ci_alpha	alpha (opacity) for fill in confidence limits
ci_border	color (or flag to turn off and on) the border of the confidence limits
ci_col	color for border of confidence limits
plot_args	list of additional arguments to pass to plot(), which sets up the plot frame
polygon_args	list of additional arguments to pass to graphics::polygon(), which fills the confidence limits
lines_args	list of additional arguments to pass to graphics::lines(), which plots the mean
abline_args	list of additional arguments to pass to graphics::abline(), which plots the minimum
index	an optional index, to plot only one (the index-th) set of the parameter combinations.
measure	any of the measures used in the call to trainSLOPE(). If measure = "auto" then deviance will be used for binomial and multinomial models, whilst mean-squared error will be used for Gaussian and Poisson models.
...	ignored

Value

A plot for every value of q is produced on the current device.

See Also

trainSLOPE()

Other model-tuning: cvSLOPE(), trainSLOPE()

Examples

# Cross-validation for a SLOPE binomial model
set.seed(123)
tune <- cvSLOPE(subset(mtcars, select = c("mpg", "drat", "wt")),
  mtcars$hp,
  q = c(0.1, 0.2),
  n_folds = 10
)
plot(tune, ci_col = "salmon", index = 1)

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