layerCor: Correlation and (weighted) covariance

In terra: Spatial Data Analysis

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 signature 'SpatRaster'
layerCor(x, fun, w, asSample=TRUE, na.rm=FALSE, maxcell=Inf, ...)

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	postive 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)

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.

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.

See Also

global, cov.wt, weighted.mean

Examples

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)

terra documentation built on April 22, 2023, 1:16 a.m.