Compute principal components for SpatRaster layers. This method may be preferred to princomp for its greater numerical accuracy. However, it is slower and for very large rasters it can only be done with a sample. This may be good enough but see princomp if you want to use all values. Unlike princomp, in this method the sample variances are used with n-1 as the denominator.
# S4 method for SpatRaster
prcomp(x, retx=TRUE, center=TRUE, scale.=FALSE,
tol=NULL, rank.=NULL, maxcell=Inf)prcomp object
SpatRaster
a logical value indicating whether the rotated variables should be returned
a logical value indicating whether the variables should be shifted to be zero centered. Alternately, a vector of length equal the number of columns of x can be supplied. The value is passed to scale
a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is FALSE for consistency with S, but in general scaling is advisable. Alternatively, a vector of length equal the number of columns of x can be supplied. The value is passed to scale
a value indicating the magnitude below which components should be omitted. (Components are omitted if their standard deviations are less than or equal to tol times the standard deviation of the first component.) With the default null setting, no components are omitted (unless rank. is specified less than min(dim(x))). Other settings for tol could be tol = 0 or tol = sqrt(.Machine$double.eps), which would omit essentially constant components
optionally, a number specifying the maximal rank, i.e., maximal number of principal components to be used. Can be set as alternative or in addition to tol, useful notably when the desired rank is considerably smaller than the dimensions of the matrix
positive integer. The maximum number of cells to be used. If this is smaller than ncell(x), a regular sample of x is used
f <- system.file("ex/logo.tif", package = "terra")
r <- rast(f)
pca <- prcomp(r)
x <- predict(r, pca)
# use "index" to get a subset of the components
p <- predict(r, pca, index=1:2)
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