Plot for Box's M test with bootstrap CIs — plot_boxM

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()

Value

Invisibly returns the confidence interval data frame (if computed)

Details

This implementation is still Experimental

Examples

if (FALSE) { # \dontrun{
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)
} # }