parCov: Efficient calculation of covariance matrices for Raster*...
In CENFA: Climate and Ecological Niche Factor Analysis

Description Usage Arguments Details Value See Also Examples

parCov efficiently calculates the covariance of Raster* objects, taking advantage of parallel processing and pulling data into memory only as necessary. For large datasets with lots of variables, calculating the covariance matrix rapidly becomes unwieldy, as the number of calculations required grows quadratically with the number of variables.

parCov(x, y, ...)

## S4 method for signature 'Raster,missing'
parCov(
  x,
  w = NULL,
  sample = TRUE,
  progress = FALSE,
  parallel = FALSE,
  n = 1,
  cl = NULL,
  keep.open = FALSE
)

## S4 method for signature 'Raster,Raster'
parCov(
  x,
  y,
  w = NULL,
  sample = TRUE,
  progress = FALSE,
  parallel = FALSE,
  n = 1,
  cl = NULL,
  keep.open = FALSE
)

`x`	Raster* object, typically a brick or stack
`y`	NULL (default) or a Raster* object with the same extent and resolution as `x`
`...`	additional arguments, including any of the following:
`w`	optional Raster* object of weights for a weighted covariance matrix
`sample`	logical. If `TRUE`, the sample covariance is calculated with a denominator of $n-1$
`progress`	logical. If `TRUE`, messages and progress bar will be printed
`parallel`	logical. If `TRUE` then multiple cores are utilized
`n`	numeric. Number of CPU cores to utilize for parallel processing
`cl`	optional cluster object
`keep.open`	logical. If `TRUE` and `parallel = TRUE`, the cluster object will not be closed after the function has finished

This function is designed to work similarly to the cov and the layerStats functions, with two major differences. First, parCov allows you to calculate the covariance between two different Raster* objects, whereas layerStats does not. Second, parCov can (optionally) compute each element of the covariance matrix in parallel, offering a dramatic improvement in computation time for large Raster* objects.

The raster layer of weights w should contain raw weights as values, and should not be normalized so that sum(w) = 1. This is necessary for computing the sample covariance, whose formula contains sum(w) - 1 in its denominator.

Returns a matrix with the same row and column names as the layers of x. If y is supplied, then the covariances between the layers of x and the layers of codey are computed.

cov, layerStats

mat1 <- parCov(climdat.hist)

# correlation matrix
Z <- parScale(climdat.hist)
mat2 <- parCov(Z)

# covariance between two Raster* objects
mat3 <- parCov(x = climdat.hist, y = climdat.fut)