| predict.enfa {adehabitatHS} | R Documentation |
Habitat Suitability Maps Built from the ENFA
Description
predict.enfa computes habitat suitability maps using the
Ecological-Niche Factor Analysis and the Mahalanobis distances
method.
Usage
## S3 method for class 'enfa'
predict(object, map, nf, ...)
Arguments
object |
an object of class |
map |
an object of class |
nf |
the number of axes of specialization kept for the
predictions. By default, all axes kept in |
... |
further arguments passed to or from other methods |
Details
The predictions are based on the position of the niche defined by the
ENFA within the multidimensional space of environmental variables. The
ENFA produces row coordinates for each pixel, which are used with the
function mahalanobis. For each pixel, this function computes the
Mahalanobis distances from the barycentre of the niche.
Actually, the function predict.enfa is identical to the function
mahasuhab, except that the habitat suitability map is computed
using the axes of the ENFA, instead of the raw data.
Note that the MADIFA allows a more consistent factorial decomposition of the Mahalanobis distances.
Value
Returns a raster map of class SpatialPixelsDataFrame.
Author(s)
Mathieu Basille basille@ase-research.org
References
Clark, J.D., Dunn, J.E. and Smith, K.G. (1993) A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management, 57, 519–526.
Hirzel, A.H., Hausser, J., Chessel, D. & Perrin, N. (2002) Ecological-niche factor analysis: How to compute habitat-suitability maps without absence data? Ecology, 83, 2027–2036.
See Also
mahalanobis for information on the computation of
Mahalanobis distances. mahasuhab for more details on the
computation of habitat suitability maps using the Mahalanobis distances.
madifa for a more consistent factorial decomposition of
the Mahalanobis distances
Examples
## Not run:
data(lynxjura)
map <- lynxjura$map
## We keep only "wild" indices.
locs <- lynxjura$locs
locs <- locs[slot(locs, "data")[,2]!="D",]
pr <- slot(count.points(locs, map), "data")[,1]
(enfa1 <- enfa(dudi.pca(slot(map, "data"), scannf=FALSE),
pr, scannf = FALSE))
## Compute the prediction
pred <- predict(enfa1, map)
image(pred)
contour(pred, col="green", add=TRUE)
points(locs, pch = 3)
## End(Not run)