Apply a principal component analysis (PCA) to layers of a GRaster

This function applies a principal component analysis to layers of a GRaster.

Usage

# S4 method for class 'GRaster'
pca(x, scale = TRUE, scores = FALSE)

Arguments

x

A GRaster with two or more layers.

scale

Logical: If TRUE (default), input layers will be rescaled by dividing each layer by its overall population standard deviation. Rasters will always be centered (have their mean subtracted from values). Centering and scaling is recommended when rasters values are in different units.

scores

Logical: If TRUE, the prcomp object will have the scores attached to it. This can greatly increase the size of the object in memory if the input raster has many cells. It will also take more time. If FALSE (default), then skip returning scores.

Value

A multi-layer GRaster with one layer per principal component axis. The pcs() function can be used on the output raster to retrieve a prcomp object from the raster, which includes rotations (loadings) and proportions of variance explained.

See also

stats::prcomp(); module i.pca in GRASS

Examples

if (grassStarted()) {

# Setup
library(terra)

# Climate raster:
madChelsa <- fastData("madChelsa")

# Convert a SpatRaster to a GRaster:
chelsa <- fast(madChelsa)

# Generate raster with layers representing principal component predictions:
pcRast <- pca(chelsa, scale = TRUE)
plot(pcRast)

# Get information on the PCA:
prinComp <- pcs(pcRast)

prinComp
summary(prinComp)
plot(prinComp)

}