# layerStats

0th

Percentile

##### 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).

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

...

##### Value

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

##### 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.

cellStats, cov.wt, weighted.mean

• layerStats
##### Examples
# NOT RUN {
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)
# }

Documentation reproduced from package raster, version 2.9-5, License: GPL (>= 3)

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