plot.quantile.bspcov: Plot method for quantile.bspcov objects

Description

Create visualization plots for posterior quantiles of covariance matrices. Produces heatmap visualizations showing uncertainty across different quantile levels.

Usage

# S3 method for quantile.bspcov
plot(
  x,
  type = "heatmap",
  titles = NULL,
  ncol = 3,
  color_limits = NULL,
  color_low = "black",
  color_high = "white",
  base_size = 14,
  legend_position = "bottom",
  x_label = "Variable",
  y_label = "Variable",
  width = NULL,
  height = 6,
  ...
)

Value

A ggplot object (single quantile) or patchwork object (multiple quantiles) showing heatmap visualizations.

Arguments

x: an object of class quantile.bspcov from quantile() function.
type: character string specifying plot type. Options are "heatmap" (default), "uncertainty", or "comparison".
titles: character vector of titles for each quantile plot. If NULL, auto-generated titles are used.
ncol: number of columns in the plot layout. Default is 3.
color_limits: optional vector of length 2 specifying color scale limits. If NULL, computed from data.
color_low: color for low values in heatmap. Default is "black".
color_high: color for high values in heatmap. Default is "white".
base_size: base font size for plot theme. Default is 14.
legend_position: position of legend. Default is "bottom".
x_label: label for x-axis. Default is "Variable".
y_label: label for y-axis. Default is "Variable".
width: plot width when saving. Default is calculated based on number of quantiles.
height: plot height when saving. Default is 6.
...: additional arguments passed to plotting functions.

Author

Kyeongwon Lee

Examples

Run this code

# \donttest{
# Example with simulated data
n <- 25
p <- 50
Sigma0 <- diag(1, p)
X <- MASS::mvrnorm(n = n, mu = rep(0, p), Sigma = Sigma0)
res <- bspcov::bandPPP(X, 2, 0.01, nsample = 100)
quant <- quantile(res)

# Plot uncertainty visualization
plot(quant)

# Plot with custom titles and labels
plot(quant, titles = c("Lower Bound", "Median", "Upper Bound"),
     x_label = "Variable", y_label = "Variable")
# }