| methods-princomp {ordr} | R Documentation |
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 class 'princomp'
as_tbl_ord(x)
## S3 method for class 'princomp'
recover_rows(x)
## S3 method for class 'princomp'
recover_cols(x)
## S3 method for class 'princomp'
recover_inertia(x)
## S3 method for class 'princomp'
recover_coord(x)
## S3 method for class 'princomp'
recover_conference(x)
## S3 method for class 'princomp'
recover_aug_rows(x)
## S3 method for class 'princomp'
recover_aug_cols(x)
## S3 method for class 'princomp'
recover_aug_coord(x)
Arguments
x |
An ordination object. |
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.
Author(s)
Emily Paul
See Also
Other methods for singular value decomposition-based techniques:
methods-cancor,
methods-correspondence,
methods-lda,
methods-lra,
methods-mca,
methods-prcomp,
methods-svd
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))