Plot results from cross-validation

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 cvSLOPE()

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

cvSLOPE()

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)