geoDist: Geographic distance model

In dismo: Species Distribution Modeling

Geographic distance model

Description

The geographic distance model predicts that the likelyhood of presence is highest near places where a species has been observed. It can be used as a null-model to calibrate cross-validation scores with.

The predicted values are the inverse distance to the nearest known presence point. Distances smaller than or equal to zero are set to 1 (highest score).

Usage

geoDist(p, ...)

Arguments

p	point locations (presence). Two column matrix, data.frame or SpatialPoints* object
...	Additional arguments. You must supply a lonlat= argument (logical), unless p is a SpatialPoints* object and has a valid CRS (coordinate reference system). You can also supply an additional argument 'a' for absence points (currently ignored). Argument 'a' should be of the same class as argument 'p'

Value

An object of class 'GeographicDistance' (inherits from DistModel-class)

Author(s)

Robert J. Hijmans

See Also

predict, convHull, maxent, domain, mahal, voronoiHull, geoIDW

Examples

r <- raster(system.file("external/rlogo.grd", package="raster"))
#presence data
pts <- matrix(c(17, 42, 85, 70, 19, 53, 26, 84, 84, 46, 48, 85, 4, 95, 48, 54, 66, 74, 50, 48, 
        28, 73, 38, 56, 43, 29, 63, 22, 46, 45, 7, 60, 46, 34, 14, 51, 70, 31, 39, 26), ncol=2)
colnames(pts) <- c('x', 'y')

train <- pts[1:12, ]
test <- pts[13:20, ]
				 
gd <- geoDist(train, lonlat=FALSE)
p <- predict(gd, r)

## Not run: 
plot(p)
points(test, col='black', pch=20, cex=2)
points(train, col='red', pch=20, cex=2)

## End(Not run)

dismo documentation built on May 31, 2023, 6:15 p.m.