ordr (version 0.1.1)

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

Description

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

Usage

# S3 method for prcomp
as_tbl_ord(x)
# S3 method for prcomp
recover_rows(x)
# S3 method for prcomp
recover_cols(x)
# S3 method for prcomp
recover_inertia(x)
# S3 method for prcomp
recover_coord(x)
# S3 method for prcomp
recover_conference(x)
# S3 method for prcomp
recover_aug_rows(x)
# S3 method for prcomp
recover_aug_cols(x)
# S3 method for prcomp
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

See Also

Other methods for singular value decomposition-based techniques: methods-cancor, methods-correspondence, methods-lda, methods-lra, methods-mca, methods-princomp, methods-svd

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

Examples

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

# compute scaled row-principal components of scaled measurements
iris[, -5] %>%
  prcomp(scale = TRUE) %>%
  as_tbl_ord() %>%
  print() -> iris_pca

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

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