predicts (version 0.1-17)

pwd_sample: Pair-wise distance sampling

Description

Select pairs of points from two sets (without replacement) that have a similar distance to their nearest point in another set of points.

For each point in "fixed", a point is selected from "sample" that has a similar distance (as defined by threshold) to its nearest point in "reference" (note that these are likely to be different points in reference). The select point is either the nearest point nearest=TRUE, or a randomly select point nearest=FALSE that is within the threshold distance. If no point within the threshold distance is found in sample, the point in fixed is dropped.

Hijmans (2012) proposed this sampling approach to remove 'spatial sorting bias' from evaluation data used in cross-validation of presence-only species distribution models. In that context, fixed are the testing-presence points, sample the testing-absence (or testing-background) points, and reference the training-presence points.

Usage

pwd_sample(fixed, sample, reference, tr=0.33, nearest=TRUE, n=1, lonlat=TRUE, warn=TRUE)

Value

A matrix of nrow(fixed) and ncol(n), that indicates, for each point (row) in fixed which point(s) in sample it is paired to; or NA if no suitable pair was available.

Arguments

fixed

two column matrix (x, y) or (longitude/latitude) or SpatialPoints object, for point locations for which a pair should be found in sample

sample

as above for point locations from which to sample to make a pair with a point from fixed

reference

as above for reference point locations to which distances are computed

n

How many pairs do you want for each point in fixed

tr

Numeric, normally below 1. The threshold distance for a pair of points (one of fixed and one of sample) to their respective nearest points in reference to be considered a valid pair. The absolute difference in distance between the candidate point pairs in fixed and reference (dfr) and the distance between candidate point pairs in sample and reference (dsr) must be smaller than tr * dfr. I.e. if the dfr = 100 km, and tr = 0.1, dsr must be between >90 and <110 km to be considered a valid pair.

nearest

Logical. If TRUE, the pair with the smallest difference in distance to their nearest reference point is selected. If FALSE, a random point from the valid pairs (with a difference in distance below the threshold defined by tr) is selected (generally leading to higher SSB

lonlat

Logical. Use TRUE if the coordinates are spherical (in degrees), and use FALSE if they are planar

warn

Logical. If TRUE a warning is given if nrow(fixed) < nrow(sample)

References

Hijmans, R.J., 2012. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null-model. Ecology 93: 679-688

Examples

ref <- matrix(c(-54.5,-38.5, 2.5, -9.5, -45.5, 1.5, 9.5, 4.5, -10.5, -10.5), ncol=2)
fix <- matrix(c(-56.5, -30.5, -6.5, 14.5, -25.5, -48.5, 14.5, -2.5, 14.5,
               -11.5, -17.5, -11.5), ncol=2)
r <- rast()
ext(r) <- c(-110, 110, -45, 45)
r[] <- 1
set.seed(0)
sam <- spatSample(r, 50, xy=TRUE, as.points=TRUE)

plot(sam, pch='x')
points(ref, col='red', pch=18, cex=2)
points(fix, col='blue', pch=20, cex=2)

i <- pwd_sample(fix, sam, ref, lonlat=TRUE)
i
sfix <- fix[!is.na(i), ]
ssam <- sam[i[!is.na(i)], ]
ssam

plot(sam, pch='x', cex=0)
points(ssam, pch='x')
points(ref, col='red', pch=18, cex=2)
points(sfix, col='blue', pch=20, cex=2)

# try to get 3 pairs for each point in 'fixed'
pwd_sample(fix, sam, ref, lonlat=TRUE, n=3)