layerStats: Correlation and (weighted) covariance

Description

Compute correlation and (weighted) covariance for multi-layer Raster objects. Like cellStats this function returns a few values, not a Raster* object (see Summary-methods for that).

Usage

layerStats(x, stat, w, asSample=TRUE, na.rm=FALSE, ...)

Value

List with two items: the correlation or (weighted) covariance matrix, and the (weighted) means.

Arguments

x: RasterStack or RasterBrick for which to compute a statistic
stat: Character. The statistic to compute: either 'cov' (covariance), 'weighted.cov' (weighted covariance), or 'pearson' (correlation coefficient)
w: RasterLayer with the weights (should have the same extent, resolution and number of layers as x) to compute the weighted covariance
asSample: Logical. If TRUE, the statistic for a sample (denominator is n-1) is computed, rather than for the population (denominator is n)
na.rm: Logical. Should missing values be removed?
...: Additional arguments (none implemetned)

Author

Jonathan A. Greenberg & Robert Hijmans. Weighted covariance based on code by Mort Canty

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 <- brick(system.file("external/rlogo.grd", package="raster"))
layerStats(b, 'pearson')

layerStats(b, 'cov')

# weigh by column number
w <- init(b, v='col')
layerStats(b, 'weighted.cov', w=w)

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