spatialEco (version 0.1-2)

pseudo.absence: Pseudo-absence random samples

Description

Generates pseudo-absence samples based on density estimate of known locations

Usage

pseudo.absence(x, n, window = "hull", Mask = NULL, s = NULL,
  sigma = "Scott", wts = NULL, KDE = FALSE, gradient = 1, p = NULL,
  edge = FALSE)

Arguments

x

An sp class SpatialPointsDataFrame or SpatialPoints object

n

Number of random samples to generate

window

Type of window (hull OR extent), overridden if mask provided

Mask

Optional rasterLayer class mask raster. The resolution of the density estimate will match mask.

s

Optional resolution passed to window argument. Caution should be used due to long processing times associated with high resolution. In contrast, coarse resolution can exclude known points.

sigma

Bandwidth selection method for KDE, default is 'Scott'. Options are 'Scott', 'Stoyan', 'Diggle', 'likelihood', and 'geometry'

wts

Optional vector of weights corresponding to point pattern

KDE

save KDE raster (TRUE/FALSE)

gradient

A scaling factor applied to the sigma parameter used to adjust the gradient decent of the density estimate. The default is 1, for no adjustment (downweight < 1 | upweight > 1)

p

Minimum value for probability distribution (must be > 0)

edge

Apply Diggle edge correction (TRUE/FALSE)

Value

• A list class object with the following components: sample SpatialPointsDataFrame containing random samples kde sp RasterLayer class of KDE estimates (IF KDE = TRUE) sigma Selected bandwidth of KDE

References

Berman, M. and Diggle, P. (1989) Estimating weighted integrals of the second-order intensity of a spatial point process. Journal of the Royal Statistical Society, series B 51, 81-92. Fithian, W & T. Hastie (2013) Finite-sample equivalence in statistical models for presence-only data. Annals of Applied Statistics 7(4): 1917-1939 Hengl, T., H. Sierdsema, A. Radovic, and A. Dilo (2009) Spatial prediction of species distributions from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging. Ecological Modelling, 220(24):3499-3511 Loader, C. (1999) Local Regression and Likelihood. Springer, New York. Scott, D.W. (1992) Multivariate Density Estimation. Theory, Practice and Visualization. New York, Wiley. Stoyan, D. and Stoyan, H. (1995) Fractals, random shapes and point fields: methods of geometrical statistics. John Wiley and Sons. Warton, D.i., and L.C. Shepherd (2010) Poisson Point Process Models Solve the Pseudo-Absence Problem for Presence-only Data in Ecology. The Annals of Applied Statistics, 4(3):1383-1402

Examples

library(sp)
data(meuse)
  coordinates(meuse) = ~x+y

pa <- pseudo.absence(meuse, n=100, window='hull', KDE=TRUE, sigma='Diggle', s=50)
  col.br <- colorRampPalette(c('blue','yellow'))
    plot(pa$kde, col=col.br(10))
      plot(meuse, pch=20, cex=1, add=TRUE)
        plot(pa$sample, col='red', pch=20, cex=1, add=TRUE)
          legend('top', legend=c('Presence', 'Pseudo-absence'),
                 pch=c(20,20),col=c('black','red'))

# With clustered data
library(sp)
library(spatstat)
data(bei)
  trees <- as(bei, 'SpatialPoints')
    trees <- SpatialPointsDataFrame(coordinates(trees),
                        data.frame(ID=1:length(trees)))
      trees.abs <- pseudo.absence(trees, n=100, window='extent', KDE=TRUE)

col.br <- colorRampPalette(c('blue','yellow'))
  plot(trees.abs$kde, col=col.br(10))
   plot(trees, pch=20, cex=0.50, add=TRUE)
      plot(trees.abs$sample, col='red', pch=20, cex=1, add=TRUE)
        legend('top', legend=c('Presence', 'Pseudo-absence'),
               pch=c(20,20),col=c('black','red'))