climPCA: Reduce dimensionality of climate predictors via Principal...

In JorGarMol/VoCC: The Velocity of Climate Change and related climatic metrics

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

See Also

dVoCC, climPlot

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 Aug. 17, 2022, 11:07 p.m.