Geographic Distance

0th

Percentile

Geographic distance model

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).

Keywords
spatial
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)

See Also

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

Aliases
  • geoDist
  • geoDist,SpatialPoints-method
  • geoDist,matrix-method
  • geoDist,data.frame-method
  • GeographicDistance-class
Examples
# NOT RUN {
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)
# }
Documentation reproduced from package dismo, version 1.3-3, License: GPL (>= 3)

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