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).
geoDist(p, ...)
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' |
An object of class 'GeographicDistance' (inherits from DistModel-class)
Robert J. Hijmans
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.