dismo: ssb – R documentation

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ssb

Spatial sorting bias

Description

Determine "spatial sorting bias", or the difference between two point data sets in the average distance to the nearest point in a reference dataset.

Usage

ssb(p, a, reference, lonlat=TRUE, avg=TRUE)

Arguments

`p`	two column matrix (x, y) or (longitude/latitude) or SpatialPoints object, for point locations
`a`	two column matrix (x, y) or (longitude/latitude) or SpatialPoints object, for point locations
`reference`	as above for reference point locations to which distances are computed
`lonlat`	Logical. Use `TRUE` if the coordinates are spherical (in degrees), and use `FALSE` if they are planar
`avg`	Logical. If `TRUE` the distances are averaged

Value

matrix with two values. 'dp': the average distance from a point in p to the nearest point in reference and 'da': the average distance from a point in a to the nearest point in reference. Distance is in meters if lonlat=TRUE, and in mapunits (typically also meters) if lonlat=FALSE

Author(s)

Robert J. Hijmans

References

Hijmans, R.J., 2012. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null-model. Ecology 93: 679-688.

Examples

ref <- matrix(c(-54.5,-38.5, 2.5, -9.5, -45.5, 1.5, 9.5, 4.5, -10.5, -10.5), ncol=2)
p <- matrix(c(-56.5, -30.5, -6.5, 14.5, -25.5, -48.5, 14.5, -2.5, 14.5, 
        -11.5, -17.5, -11.5), ncol=2)
r <- raster()
extent(r) <- c(-110, 110, -45, 45)
r[] <- 1
set.seed(0)
a <- randomPoints(r, n=50)
b <- ssb(p, a, ref)

# distances in km
b / 1000

# an index of spatial sorting bias (1 is no bias, near 0 is extreme bias)
b[1] / b[2]

dismo

Species Distribution Modeling

v1.3-3

GPL (>= 3)

Authors

Robert J. Hijmans, Steven Phillips, John Leathwick and Jane Elith

Initial release

2020-11-16

ssb