plot.princomp.BData.multivariate: Plot Method for Multivariate SFPCA of Distributional Data

In DistributionalDDA: Distributional Data Analysis in Bayes Spaces

Plot Method for Multivariate SFPCA of Distributional Data

Description

Creates visualization plots for multivariate SFPCA (2D-SFPCA) results. Eigenfunctions span multiple groups and are shown with one panel per group.

Usage

## S3 method for class 'princomp.BData.multivariate'
plot(
  x,
  type = c("screeplot", "variance", "scores", "eigenfunctions", "loadings"),
  npcs = 2,
  pcs = c(1, 2),
  as = "PDF",
  ...
)

Arguments

x	An object of class 'princomp.BData.multivariate' as returned by princomp.BData with 'group' and 'multivariate = TRUE'.
type	Character string specifying the type of plot: "screeplot" Variance explained by each component "scores" Scatter plot of PC scores "eigenfunctions" Eigenfunctions shown per group (default) "loadings" Alias for eigenfunctions
npcs	Number of principal components to display. Default is 2.
pcs	Which PCs to use for scores plot. Default is c(1, 2).
as	Representation for eigenfunction plots: "PDF", "clr", "density", etc.
...	Additional graphical arguments.

Details

For multivariate SFPCA, eigenfunctions are joint across all groups. The eigenfunctions plot shows how each group's component of the eigenfunction affects the distribution shape.

Value

Invisibly returns the multivariate PCA object.

Author(s)

Matthias Templ

See Also

princomp.BData for performing multivariate SFPCA.

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

Examples

data("age")
basisAge <- BayesSpaceBasis(BayesSpace(ReferenceMeasureUniform(0, 100)), nbasis = 7)
breaks <- c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 100)
ageHist <- BDataDensityHistograms(age$distributions, breaks = breaks, basis = basisAge)
ageSplines <- as.BDataSplines(as.BDataDensities(ageHist))
pcaMulti <- princomp(ageSplines, group = age$data$sex, multivariate = TRUE)
plot(pcaMulti, type = "eigenfunctions", npcs = 2)

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