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Reconstruct data from an sfpca fit

Source: R/sfpca.R
reconstruct.sfpca.Rd

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.

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.

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.

See also

sfpca()