layerCor: Correlation and (weighted) covariance

Description

Compute correlation, (weighted) covariance, or similar summary statistics that compare the values of all pairs of the layers of a SpatRaster.

Usage

# S4 method for SpatRaster
layerCor(x, fun, w, asSample=TRUE, na.rm=FALSE, maxcell=Inf, ...)

Value

If fun is one of the three standard statistics, you get a list with two items: the correlation or (weighted) covariance matrix, and the (weighted) means.

If fun is a function, you get a matrix.

Arguments

x: SpatRaster
fun: character. The statistic to compute: either "cov" (covariance), "weighted.cov" (weighted covariance), or "pearson" (correlation coefficient) or your own function that takes two vectors as argument to compute a single number
w: SpatRaster with the weights to compute the weighted covariance. It should have a single layer and the same geometry as x
asSample: logical. If TRUE, the statistic for a sample (denominator is n-1) is computed, rather than for the population (denominator is n). Only for the standard functions
na.rm: logical. Should missing values be removed?
maxcell: positive integer. The number of cells to be regularly sampled. Only used when fun is a function
...: additional arguments for fun (if it is a proper function)

References

For the weighted covariance:

Canty, M.J. and A.A. Nielsen, 2008. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation. Remote Sensing of Environment 112:1025-1036.
Nielsen, A.A., 2007. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data. IEEE Transactions on Image Processing 16(2):463-478.

Examples

Run this code

b <- rast(system.file("ex/logo.tif", package="terra"))   
layerCor(b, "pearson")

layerCor(b, "cov")

# weigh by column number
w <- init(b, fun="col")
layerCor(b, "weighted.cov", w=w)