Supplementary individual diagnostics — pca_supplementary

Compute principal component scores and quality metrics for supplementary individuals (rows) projected into an existing PCA solution.

Usage

pca_supplementary_individuals(
  data,
  rotation,
  sdev,
  center = NULL,
  scale = NULL,
  total_weight = NA_real_
)

data: Matrix-like object whose rows correspond to supplementary individuals and columns to the original variables.
rotation: Rotation matrix from the PCA model (e.g. the rotation element of a bigpca result).
sdev: Numeric vector of component standard deviations associated with rotation.
center: Optional numeric vector giving the centring applied to each variable when fitting the PCA. Defaults to zero centring.
scale: Optional numeric vector describing the scaling applied to each variable when fitting the PCA. When NULL, no scaling is applied.
total_weight: Optional positive scalar passed to pca_individual_contributions() when computing contributions. When left as NA (the default), the resulting contributions for each component are normalised to sum to one across supplementary individuals. Supplying a value bypasses this normalisation and delegates the scaling to pca_individual_contributions().

A list with elements scores, contributions, and cos2.