SLOPE (version 0.3.2)

plot.TrainedSLOPE: Plot results from cross-validation

Description

Plot results from cross-validation

Usage

# S3 method for TrainedSLOPE
plot(
  x,
  measure = c("auto", "mse", "mae", "deviance", "auc", "misclass"),
  plot_min = TRUE,
  ci_alpha = 0.2,
  ci_border = FALSE,
  ci_col = lattice::trellis.par.get("superpose.line")$col,
  ...
)

Arguments

x

an object of class 'TrainedSLOPE', typically from a call to trainSLOPE()

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.

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

...

other arguments that are passed on to lattice::xyplot()

Value

An object of class "trellis" is returned and, if used interactively, will most likely have its print function lattice::print.trellis() invoked, which draws the plot on the current display device.

See Also

trainSLOPE(), lattice::xyplot(), lattice::panel.xyplot()

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

Examples

# NOT RUN {
# Cross-validation for a SLOPE binomial model
set.seed(123)
tune <- trainSLOPE(subset(mtcars, select = c("mpg", "drat", "wt")),
                   mtcars$hp,
                   q = c(0.1, 0.2),
                   number = 10)
plot(tune, ci_col = "salmon", col = "black")
# }

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