Constructs low-rank sample-space scores without forming a p by p covariance
matrix. This is useful when the number of predictors is very large.
Usage
dual_pca_scores(
X,
dual_rank = NULL,
variance_explained = NULL,
center = TRUE,
scale_X = TRUE,
eps = 1e-10
)
Arguments
- X
Numeric predictor matrix with observations in rows.
- dual_rank
Number of dual components to retain.
- variance_explained
Optional cumulative variance threshold.
- center
Logical; if TRUE, centre predictors.
- scale_X
Logical; if TRUE, scale predictors.
- eps
Numerical tolerance.
Value
A list containing scores, loadings, singular values, centre, scale,
and retained rank.