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

In ordr: A 'tidyverse' Extension for Ordinations and Biplots

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 class 'prcomp'
as_tbl_ord(x)

## S3 method for class 'prcomp'
recover_rows(x)

## S3 method for class 'prcomp'
recover_cols(x)

## S3 method for class 'prcomp'
recover_inertia(x)

## S3 method for class 'prcomp'
recover_coord(x)

## S3 method for class 'prcomp'
recover_conference(x)

## S3 method for class 'prcomp'
recover_aug_rows(x)

## S3 method for class 'prcomp'
recover_aug_cols(x)

## S3 method for class 'prcomp'
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-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))

ordr documentation built on Oct. 21, 2022, 1:07 a.m.