climPCA: Reduce dimensionality of climate predictors via Principal...
In JorGarMol/VoCC: The Velocity of Climate Change and related climatic metrics

climPCA

R Documentation

Reduce dimensionality of climate predictors via Principal Component Analysis

Description

Function to extract the first n principal components explaining a predefined total amount of variance among climatic variables. These components can subsequently be used as synthetic climatic variables to reduce dimensionality in climate-analogue methods.

Usage

climPCA(climp, climf, trans = function(x) log(x), cen = TRUE, sc = TRUE, th = 0.8)

Arguments

`climp`	`raster.stack` with one layer for each climatic variable with the values for present or baseline conditions.
`climf`	`raster.stack` with one layer for each climatic variable with the values for future conditions.
`trans`	`function` specifying the type of transformation to be applied prior to the PCA. Specify NA where no transformation is required (default log(x)).
`cen`	`logical` should the variables be centered prior to the PCA? (default TRUE).
`sc`	`logical` should the variables be scaled prior to the PCA? (default TRUE).
`th`	`numeric` threshold giving the minimum amount of total variance that should be explained by the principal components extracted.

Value

a list containing (i) the output from the PCA (call to 'prcomp'), and (ii) a table with the present/future cell values for the principal components accounting for the specified percentage of total variance (th).

Author(s)

Jorge Garcia Molinos

Examples


comp <- climPCA(JapTC[[c(1,3,5)]], JapTC[[c(2,4,6)]], trans = NA, cen = TRUE, sc = TRUE, th = 0.85)
summary(comp[[1]]) # first two components explain >90% of variance
# Create a data frame with the necessary variables in the required order (see climAna? for details)
clim <- comp[[2]][,c(2,4,3,5,1)]
clim[,c("x","y")] <- xyFromCell(JapTC[[1]], clim$cid)

JorGarMol/VoCC documentation built on Feb. 1, 2025, 7:53 p.m.