pca: Principal Components Analysis

In ISAAKiel/quantAAR: R Library for Quantitative Analysis in Archaeology

Description

Principal Components Analysis function wrappers that give the result in a tidy data.frame.

Usage

pca.stats_prcomp(..., raw_output = TRUE)

pca.vegan_rda(..., raw_output = TRUE)

Arguments

...	Input arguments of the relevant wrapped functions.
raw_output	Logical. Should the raw output of the wrapped functions be stored as an additional output attribute "raw"? Default: TRUE.

Value

A tibble with the PCA results for variables (columns) and objects (rows). Additional values are stored in object attributes. See attributes(result)$raw.

name: Character. Names of rows and columns.

type: Character. Type of entry ("row" or "col").

...: Additional variables as provided by the wrapped functions.

PC1...PCX: Numeric. Resulting coordinates.

Examples

# establish an other distance measure in the data that allows application of PCA
matuskovo_distance <- vegan::decostand(matuskovo_material, "norm")

pca.stats_prcomp(matuskovo_distance)
pca.vegan_rda(matuskovo_distance)

ISAAKiel/quantAAR documentation built on July 12, 2020, 4:16 p.m.