Correlation and (weighted) covariance
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).
layerStats(x, stat, w, asSample=TRUE, na.rm=FALSE, ...)
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 |
asSample |
Logical. If |
na.rm |
Logical. Should missing values be removed? |
... |
Additional arguments (none implemetned) |
List with two items: the correlation or (weighted) covariance matrix, and the (weighted) means.
Jonathan A. Greenberg & Robert Hijmans. Weighted covariance based on code by Mort Canty
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.
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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.