reconstruct.sfpca: Reconstruct data from an sfpca fit

In genpca: Generalized Principal Component Analysis

Reconstruct data from an sfpca fit

Description

Reconstructs the rank-K sfpca model as U D V', using the stored (non-orthogonal) factors directly.

Usage

## S3 method for class 'sfpca'
reconstruct(
  x,
  comp = seq_len(multivarious::ncomp(x)),
  rowind = NULL,
  colind = NULL,
  ...
)

Arguments

x	An sfpca object.
comp	Integer vector of components to use (default: all).
rowind	Optional integer vector of rows to reconstruct (default: all).
colind	Optional integer vector of columns to reconstruct (default: all).
...	Ignored.

Details

sfpca components are Euclidean unit vectors but are not mutually orthogonal, so V'V \ne I. The inherited reconstruct.bi_projector() method reconstructs through the Moore-Penrose pseudoinverse of the loadings (⁠scores \%*\% pinv(V)⁠), which for non-orthogonal V does not return the rank-comp model U D V' that sfpca() actually fits and deflates with. This method instead computes ⁠scores(x)[rowind, comp] \%*\% t(components(x)[colind, comp])⁠, i.e. U D V' restricted to the requested rows/columns/components. sfpca() does no preprocessing, so no inverse transform is applied.

Value

A numeric matrix of dimension ⁠length(rowind) x length(colind)⁠, the rank-length(comp) reconstruction U D V' using sfpca's stored non-orthogonal factors.

See Also

sfpca()

genpca documentation built on Sept. 17, 2026, 1:09 a.m.