cvSummaryPlot: Summary Plot
In PhilBoileau/cvCovEst: Cross-Validated Covariance Matrix Estimation

cvSummaryPlot

R Documentation

Summary Plot

Description

cvSummaryPlot() combines plots of the eigenvalues and the covariance heatmap for the optimal estimator selected by cvCovEst(), and also provides a table showing the best estimator within each class. A plot the risk of the optimal estimator's class is also provided if applicable.

Usage

cvSummaryPlot(
  dat,
  estimator,
  dat_orig,
  stat,
  k,
  leading,
  plot_type = "summary",
  cv_details,
  has_hypers,
  multi_hypers,
  abs_v,
  switch_vars,
  min_max
)

Arguments

`dat`	A named `list`. Specifically, this is the standard output of `cvCovEst`.
`estimator`	A `character` vector specifying which class of estimator to plot.
`dat_orig`	The `numeric data.frame`, `matrix`, or similar object originally passed to `cvCovEst`.
`plot_type`	A `character` detailing the type of plot. Passed to `theme_cvCovEst()`, defaults to `"risk"`
`cv_details`	Character vector summarizing key arguments passed to `cvCovEst()`.
`has_hypers`	A `character` vector containing the names of current estimators with hyperparameters.
`multi_hypers`	A `character` vector containing the names of current estimators with multiple hyperparameters.
`abs_v`	A `logical` determining if the absolute value of the matrix entries should be used for plotting the matrix heatmap. Default is `TRUE`.
`switch_vars`	A `logical`. If `TRUE`, the hyperparameters used for the x-axis and factor variables are switched. Only applies to estimators with more than one hyperparameter. Defaults to `FALSE`.
`min_max`	A `logical`. If `TRUE`, only the minimum and maximum values of the factor hyperparameter will be used. Only applies to estimators with more than one hyperparameter. Defaults to `FALSE`.