ordr (version 0.2.0)

methods-princomp: Functionality for principal components analysis ('princomp') objects

Description

These methods extract data from, and attribute new data to, objects of class "princomp" as returned by stats::princomp().

Usage

# S3 method for princomp
as_tbl_ord(x)
# S3 method for princomp
recover_rows(x)
# S3 method for princomp
recover_cols(x)
# S3 method for princomp
recover_inertia(x)
# S3 method for princomp
recover_coord(x)
# S3 method for princomp
recover_conference(x)
# S3 method for princomp
recover_supp_rows(x)
# S3 method for princomp
recover_aug_rows(x)
# S3 method for princomp
recover_aug_cols(x)
# S3 method for princomp
recover_aug_coord(x)

Value

The recovery generics recover_*() return core model components, distribution of inertia, supplementary elements, and intrinsic metadata; but they require methods for each model class to tell them what these components are.

The generic as_tbl_ord() returns its input wrapped in the 'tbl_ord' class. Its methods determine what model classes it is allowed to wrap. It then provides 'tbl_ord' methods with access to the recoverers and hence to the model components.

Arguments

x

An ordination object.

Author

Emily Paul, John Gracey

Details

Principal components analysis (PCA), as performed by stats::princomp(), relies on an eigenvalue decomposition (EVD) of the covariance matrix \(X^TX\) of a data set \(X\). stats::princomp() returns the EVD factor \(V\) as the loadings $loadings. The scores $scores are obtained as \(XV\) and are accessible as supplementary elements.

See Also

Other methods for eigen-decomposition-based techniques: methods-cmds, methods-eigen, methods-factanal

Other models from the stats package: methods-cancor, methods-cmds, methods-factanal, methods-kmeans, methods-lm, methods-prcomp

Examples

# data frame of Anderson iris species measurements
class(iris)
head(iris)

# compute unscaled row-principal components of scaled measurements
iris[, -5] %>%
  princomp() %>%
  as_tbl_ord() %>%
  print() -> iris_pca

# recover observation principal coordinates and measurement standard coordinates
head(get_rows(iris_pca))
get_cols(iris_pca)

# augment measurement coordinates with names and scaling parameters
(iris_pca <- augment_ord(iris_pca))