bigPCAcpp (version 0.9.0)

bigpca: BigPCA result objects

Description

Results returned by pca_bigmatrix(), pca_stream_bigmatrix(), and pca_robust() inherit from the bigpca class. The objects store the component standard deviations, rotation/loadings, and optional scores while recording which computational backend produced them. Standard S3 generics such as summary() and plot() are implemented for convenience.

bigpca objects are lists produced by pca_bigmatrix(), pca_stream_bigmatrix(), pca_robust(), and related helpers. They mirror the structure of base R's prcomp() outputs while tracking additional metadata for large-scale and streaming computations.

#' @seealso pca_bigmatrix(), pca_stream_bigmatrix(), pca_robust(), pca_plot_scree(), pca_plot_scores(), pca_plot_contributions(), pca_plot_correlation_circle(), and pca_plot_biplot().

Components

sdev

Numeric vector of component standard deviations.

rotation

Numeric matrix whose columns contain the variable loadings (principal axes).

center, scale

Optional numeric vectors describing the centring and scaling applied to each variable when fitting the model.

scores

Optional numeric matrix of principal component scores when computed alongside the decomposition.

column_sd

Numeric vector of marginal standard deviations for each input variable.

eigenvalues

Numeric vector of eigenvalues associated with the retained components.

explained_variance, cumulative_variance

Numeric vectors summarising the fraction of variance explained by individual components and the corresponding cumulative totals.

covariance

Sample covariance matrix used to derive the components.

nobs

Number of observations used in the decomposition.

The class also records the computation backend via attr(x, "backend"), enabling downstream methods to adjust their behaviour for streamed or robust results.

See Also

pca_bigmatrix(), pca_stream_bigmatrix(), summary.bigpca(), print.summary.bigpca(), plot.bigpca()