summary.princomp.BData: Summary of PCA for Distributional Data

In DistributionalDDA: Distributional Data Analysis in Bayes Spaces

Summary of PCA for Distributional Data

Description

Provides a summary of the principal component analysis results, showing the importance of each component in terms of standard deviation, proportion of variance explained, and cumulative proportion.

Usage

## S3 method for class 'princomp.BData'
summary(object, ...)

Arguments

object	A 'princomp.BData' object.
...	Additional arguments (currently unused).

Value

An object of class 'summary.princomp.BData' containing:

sdev

Standard deviations of the principal components.

importance

Matrix with three rows: standard deviation, proportion of variance, and cumulative proportion for each component.

Author(s)

Matthias Templ

See Also

princomp.BData

Other PCA: biplot.princomp.BData(), plot.princomp.BData(), plot.princomp.BData.grouped(), plot.princomp.BData.multivariate(), predict.princomp.BData(), princomp.BData(), screeplot.princomp.BData()

Examples

meanDist <- BDataPDFs(list(dunif), basis = B2Unif01Basis7)
gaussProc <- B2GaussianDistribution(meanDist, function(x,y) exp(-4*abs(x-y)^1.7))
pcaResult <- princomp(as.BDataSplines(simulate(gaussProc, 25, seed = 42)))
summary(pcaResult)

DistributionalDDA documentation built on Oct. 20, 2026, 5:06 p.m.