eigstatCI: Bootstrap Confidence Intervals for Eigenvalue Statistics

In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Bootstrap Confidence Intervals for Eigenvalue Statistics

Description

Computes bootstrap confidence intervals for statistics based on eigenvalues of grouped covariance matrices. This is intended for use with plot.boxM() to provide confidence intervals for measures beyond the default "logDet".

This is a convenience wrapper that extracts the necessary information from a boxM object, but still requires the original data and grouping variable since these are not stored in the boxM object.

Usage

eigstatCI(
  Y,
  group,
  which = c("product", "sum", "precision", "max"),
  R = 1000,
  conf = 0.95,
  type = c("perc", "bca", "norm", "basic"),
  parallel = FALSE,
  ncpus = 2,
  seed = NULL,
  ...
)

eigstatCI_boxM(boxm, Y, group, ...)

Arguments

Y	Original data matrix used in boxM()
group	Original grouping variable used in boxM()
which	The eigenvalue-based statistic to compute. One of: product Product of eigenvalues (= determinant) sum Sum of eigenvalues (= trace) precision Harmonic mean of eigenvalues max Maximum eigenvalue
R	Number of bootstrap replicates. Default is 1000.
conf	Confidence level for intervals (0 < conf < 1). Default is 0.95.
type	Type of bootstrap confidence interval. Options: perc Percentile method (default, most robust) bca Bias-corrected and accelerated norm Normal approximation basic Basic bootstrap
parallel	Logical or character string. If TRUE, use parallel processing via the boot package. Can also be "multicore" or "snow". Default is FALSE.
ncpus	Number of CPUs to use if parallel=TRUE. Default is 2.
seed	Random seed for reproducibility. If NULL, no seed is set.
...	Additional arguments passed to eigstatCI()
boxm	A "boxM" object from boxM()

Details

For each group (and the pooled data), this function performs nonparametric bootstrap resampling to estimate the sampling distribution of the specified eigenvalue-based statistic. Confidence intervals are computed using the percentile method or bias-corrected and accelerated (BCa) method.

Unlike logdetCI() which uses analytic approximations based on asymptotic theory, this function makes no distributional assumptions and can handle small to moderate sample sizes, though computational cost increases with the number of bootstrap replicates.

Value

A data frame with one row for each group plus the pooled data. Columns include:

group

Group name (factor level)

statistic

Observed value of the statistic

lower

Lower confidence limit

upper

Upper confidence limit

bias

Bootstrap estimate of bias (if available)

se

Bootstrap standard error (if available)

A data frame with bootstrap confidence intervals

Author(s)

Michael Friendly

References

Efron, B., & Tibshirani, R. J. (1994). An Introduction to the Bootstrap. CRC Press.

See Also

boxM, plot.boxM, logdetCI

Other diagnostic plots: cqplot(), distancePlot(), plot.boxM()

Examples

## Not run: 
library(boot)
data(iris)

# Bootstrap CI for product of eigenvalues
CI_prod <- eigstatCI(iris[,1:4], iris$Species, which="product", R=500)
CI_prod

# Bootstrap CI for sum of eigenvalues (= trace)
CI_sum <- eigstatCI(iris[,1:4], iris$Species, which="sum", R=500)
CI_sum

# Use with parallel processing for speed
CI_max <- eigstatCI(iris[,1:4], iris$Species, which="max",
                    R=1000, parallel=TRUE, ncpus=4)

## End(Not run)

## Not run: 
library(boot)
data(iris)

# Fit boxM
res <- boxM(iris[,1:4], iris$Species)

# Get bootstrap CIs (must provide original data again)
CI <- eigstatCI_boxM(res, Y = iris[,1:4], group = iris$Species,
                     which = "sum", R = 500)

## End(Not run)

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