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Determine "spatial sorting bias", or the difference between two point data sets in the average distance to the nearest point in a reference dataset.
ssb(p, a, reference, lonlat=TRUE, avg=TRUE)
two column matrix (x, y) or (longitude/latitude) or SpatialPoints object, for point locations
two column matrix (x, y) or (longitude/latitude) or SpatialPoints object, for point locations
as above for reference point locations to which distances are computed
Logical. Use TRUE
if the coordinates are spherical (in degrees), and use FALSE
if they are planar
Logical. If TRUE
the distances are averaged
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
Hijmans, R.J., 2012. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null-model. Ecology 93: 679-688.
# 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[1] / b[2]
# }
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