# ssb

##### 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

##### 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[1] / b[2]
# }
```

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