# 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.

pwdSample

• ssb
##### Examples
# NOT RUN {
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 / b
# }

Documentation reproduced from package dismo, version 1.3-3, License: GPL (>= 3)

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