dismo (version 1.3-14)

mess: Multivariate environmental similarity surfaces (MESS)

Description

Compute multivariate environmental similarity surfaces (MESS), as described by Elith et al., 2010

Usage

mess(x, v, full=FALSE, filename='', ...)

Value

A RasterBrick with layers corresponding to the input layers and an additional layer with the mess values (if full=TRUE and nlayers(x) > 1) or a RasterLayer with the MESS values (if full=FALSE).

Arguments

x

Raster* object

v

matrix or data.frame containing the reference values. Each column should correspond to one layer of the Raster* object

full

logical. If FALSE a RasterLayer with the MESS values is returned. If TRUE, a RasterBrick is returned with n layers corresponding to the layers of the input Raster object and an additional layer with the MESS values

filename

character. Output filename (optional)

...

additional arguments as for writeRaster

Author

Jean-Pierre Rossi <[e-mail]>, Robert Hijmans, Paulo van Breugel

Details

v can be obtained for a set of points using extract .

References

Elith J., M. Kearney M., and S. Phillips, 2010. The art of modelling range-shifting species. tools:::Rd_expr_doi("10.1111/j.2041-210X.2010.00036.x")Methods in Ecology and Evolution 1:330-342.

Examples

set.seed(9)
r <- raster(ncol=10, nrow=10)
r1 <- setValues(r, (1:ncell(r))/10 + rnorm(ncell(r)))
r2 <- setValues(r, (1:ncell(r))/10 + rnorm(ncell(r)))
r3 <- setValues(r, (1:ncell(r))/10 + rnorm(ncell(r)))
s <- stack(r1,r2,r3)
names(s) <- c('a', 'b', 'c')
xy <- cbind(rep(c(10,30,50), 3), rep(c(10,30,50), each=3))
refpt <- extract(s, xy)

ms <- mess(s, refpt, full=TRUE)
plot(ms)


if (FALSE) {
filename <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
bradypus <- read.table(filename, header=TRUE, sep=',')
bradypus <- bradypus[,2:3]
files <- list.files(path=paste(system.file(package="dismo"),'/ex', sep=''), 
   pattern='grd', full.names=TRUE )
predictors <- stack(files)
predictors <- dropLayer(x=predictors,i=9)
reference_points <- extract(predictors, bradypus)
mss <- mess(x=predictors, v=reference_points, full=TRUE)
plot(mss)
}