heplots (version 1.8.4)

plot_boxM_boot: 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,
  ...
)

Value

Invisibly returns the confidence interval data frame (if computed)

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

Examples

if (FALSE) {
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)
}