ssb

0th

Percentile

Spatial sorting bias

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

Keywords
spatial
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

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.

See Also

pwdSample

Aliases
  • ssb
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]
Documentation reproduced from package dismo, version 1.1-4, License: GPL (>= 3)

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