plot_boxM_boot: Plot for Box's M test with bootstrap CIs

In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Plot for Box's M test with bootstrap CIs

Description

Enhanced version of plot.boxM() that supports bootstrap confidence intervals for eigenvalue-based statistics in addition to the existing analytic CIs for log determinants.

Usage

plot_boxM_boot(
  x,
  Y = NULL,
  group = NULL,
  gplabel = NULL,
  which = c("logDet", "product", "sum", "precision", "max"),
  log = which == "product",
  pch = c(16, 15),
  cex = c(2, 2.5),
  col = c("blue", "red"),
  rev = FALSE,
  xlim,
  conf = 0.95,
  method = 1,
  bias.adj = TRUE,
  lwd = 2,
  boot.R = 1000,
  boot.type = c("perc", "bca", "norm", "basic"),
  boot.parallel = FALSE,
  boot.ncpus = 2,
  boot.seed = NULL,
  ...
)

Arguments

x	A "boxM" object resulting from boxM()
Y	Optional data matrix (required for bootstrap CIs with eigenvalue stats)
group	Optional grouping variable (required for bootstrap CIs with eigenvalue stats)
gplabel	Character string used to label the group factor
which	Measure to be plotted
log	Logical; if TRUE, the log of the measure is plotted
pch	Point symbols for groups and pooled data
cex	Character size of point symbols
col	Colors for point symbols
rev	Logical; if TRUE, reverse order of groups on vertical axis
xlim	X limits for the plot
conf	Coverage for confidence intervals (0 to suppress)
method	CI method for logDet (see logdetCI())
bias.adj	Bias adjustment for logDet CIs
lwd	Line width for confidence intervals
boot.R	Number of bootstrap replicates (for eigenvalue stats only)
boot.type	Type of bootstrap CI ("perc", "bca", "norm", "basic")
boot.parallel	Use parallel processing for bootstrap
boot.ncpus	Number of CPUs for parallel bootstrap
boot.seed	Random seed for bootstrap reproducibility
...	Additional arguments passed to dotchart()

Details

This implementation is still Experimental

Value

Invisibly returns the confidence interval data frame (if computed)

Examples

## Not run: 
library(boot)
# source("dev/eigstatCI.R")
# source("dev/plot.boxM_with_bootstrap.R")

# Iris data with bootstrap CIs
boxm <- boxM(iris[,1:4], iris$Species)

# logDet with analytic CI (same as before)
plot_boxM_boot(boxm, gplabel = "Species")

# Sum of eigenvalues with bootstrap CI
plot_boxM_boot(boxm, Y = iris[,1:4], group = iris$Species,
               which = "sum", gplabel = "Species", boot.R = 1000)

## End(Not run)

heplots documentation built on Aug. 23, 2026, 5:07 p.m.