Mahalanobis model
Distribution model based on the Mahalanobis distance. The predictions are (1-distance). I.e. the highest possible value is 1, and there will likely be large negative numbers.
mahal(x, p, ...)
x |
Raster* object or matrix |
p |
two column matrix or SpatialPoints* object |
... |
Additional arguments. Currently not used |
An object of class Mahalanobis (inherits from DistModel-class)
Robert J. Hijmans
logo <- stack(system.file("external/rlogo.grd", package="raster"))
#presence data
pts <- matrix(c(48.243420, 48.243420, 47.985820, 52.880230, 49.531423, 46.182616,
54.168232, 69.624263, 83.792291, 85.337894, 74.261072, 83.792291, 95.126713,
84.565092, 66.275456, 41.803408, 25.832176, 3.936132, 18.876962, 17.331359,
7.048974, 13.648543, 26.093446, 28.544714, 39.104026, 44.572240, 51.171810,
56.262906, 46.269272, 38.161230, 30.618865, 21.945145, 34.390047, 59.656971,
69.839163, 73.233228, 63.239594, 45.892154, 43.252326, 28.356155), ncol=2)
# fit model
m <- mahal(logo, pts)
# make a prediction
predict(m, logo[1])
x <- predict(m, logo)
# or x <- predict(logo, m) via raster::predict
# plot(x > 0)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.