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

Description

Create heatmap visualization for posterior mean estimate of sparse covariance matrix. Provides flexible visualization options with customizable aesthetics and labeling.

Usage

# S3 method for postmean.bspcov
plot(
  x,
  title = NULL,
  color_limits = NULL,
  color_low = "black",
  color_high = "white",
  base_size = 14,
  legend_position = "bottom",
  x_label = "Variable",
  y_label = "Variable",
  show_values = FALSE,
  ...
)

Value

A ggplot object showing heatmap visualization of the posterior mean covariance matrix.

Arguments

x: an object of class postmean.bspcov from estimate() function.
title: character string for plot title. If NULL, auto-generated title is used.
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".
show_values: logical indicating whether to display values on tiles. Default is FALSE.
...: additional arguments passed to plotting functions.

Author

Seongil Jo, 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::thresPPP(X, eps=0.01, thres=list(value=0.5,fun='hard'), nsample=100)
est <- bspcov::estimate(res)

# Basic plot
plot(est)

# Plot with custom color scheme
plot(est, color_low = "blue", color_high = "red")
# }