alterInclProbs: Alters inclusion probabilities to accommodate legacy sites

In MBHdesign: Spatial Designs for Ecological and Environmental Surveys

Alters inclusion probabilities to accommodate legacy sites

Description

Alters inclusion probabilities to accommodate legacy sites. Inclusion probabilities are deflated around legacy sites, and the resulting set of new sites (and legacy sites) are spatially balanced.

Usage

 alterInclProbs( legacy.sites, potential.sites=NULL, n=NULL, inclusion.probs=NULL,
                                                                   mc.cores=1, sigma=NULL)

Arguments

legacy.sites	a matix (MxD) matrix of locations of the M legacy sites as points in D dimensions. For most applications D=2. Each row gives the location (in space) of one of the legacy sites.
potential.sites	a matrix (NxD) of locations of the N potential sampling sites as points in D dimension. These are the locations from which n are taken as the sample. If NULL (default) a maximum of N=10000 samples are placed on a regular grid within a convex hull defined by the legacy locations. This default may or may not make sense for you (but something has to be the default).
n	an integer specificying the number of new sites to sample. One of n and inclusion.probs needs to be specified, but if both are then n is discarded.
inclusion.probs	a vector specifying the inclusion probability for each of the N sampling sites. This is the probability that each site will be included in the final sample. The sum of inclusion.probs must be the number of new sites required. Locations in inclusion.probs must be ordered the same as the potential.sites argument. If NULL (default) equal inclusion probabilities are specified and the number of new sites is taken to be n.
mc.cores	the number of processes to for some of the calculations on (in particular the calculation of distances to legacy sites). See parLapply(qv) in package parallel.
sigma	a parameter defining the spatial influence of the legacy sites. Must be positive. It is sigma in the squared exponential decay function, exp( -distanceFromLegacySite^2 / sigma^2). If NULL (default), then it is chosen so that 95 percent of the legacy sites influence is located within the average patch size around a point. This seems like a useful default. See Foster et al. (in prep) for details.

Details

The inclusion probabilities are adjusted using a variant of the spatially clustered Poisson sampling method in Grafstrom (2012), which is itself a spatially explicit version of Bondesson and Thorburn (2008). The adjustments here are given in Foster et al. (in prep). Basically, the adjustment is similar to that proposed in Section 3.2 of Grafstrom (2012), that is the inclusion probabilities are updated using the squared loss distance metric (as above). However, the weighting function here is given by the distance times the inclusion probability of the new site.

Value

The alterInclProbs function returns a numeric vector containing inclusion probabilities adjusted for legacy sites for all of the points in the potential.sites argument.

Author(s)

Scott D. Foster

References

Bondesson, L. and Thoburn, D. (2008) A List Sequential Sampling Method Suitable for Real-Time Sampling. Scandinavian Journal of Statistics 35:466–483.

Foster, S.D., Hosack, G.R., Lawrence, E., Przeslawski, R., Hedge,P., Caley, M.J., Barrett, N.S., Williams, A., Li, J., Lynch, T., Dambacher, J.M., Sweatman, H.P.A, and Hayes, K.R. (2017) Spatially-Balanced Designs that Incorporate Legacy Sites. Methods in Ecology and Evolution 8:1433–1442.

Grafstrom, A. (2012) Spatially correlated Poisson sampling. Journal of Statistical Plannind and Inference 142:139–147.

See Also

quasiSamp, modEsti

Examples

#big plane today
set.seed(747)
#the number of potential sampling locations
N <- 50^2
#number of samples
n <- 27
#number of legacy sites
nLegacy <- 3
#the grid
X <- as.matrix( expand.grid( 1:sqrt( N), 1:sqrt(N)) / sqrt(N) - 1/(2*sqrt(N)))
#the inclusion probabiltiies with gradient according to non-linear function of X[,1]
p <- 1-exp(-X[,1])
#standardise to get n samples
p <- n * p / sum( p)
#randomly choose legacy sites
legacySites <- sample( 1:N, nLegacy, prob=p)
#alter inclusion probabilities
p2 <- alterInclProbs( legacy.sites=X[legacySites,], potential.sites=X, inclusion.probs=p)
if( requireNamespace( "graphics", quietly = TRUE)) {
  #plotting up old and new inclusion probabilities
  par( mfrow=c(1,2))
  graphics::image( x=unique( X[,1]), y=unique( X[,2]),
    z=matrix( p, nrow=sqrt(nrow(X)), ncol=sqrt(nrow( X))),
    main="Undadjusted Inclusion Probabilities", ylab="y", xlab="x")
  graphics::image( x=unique( X[,1]), y=unique( X[,2]),
    z=matrix( p2, nrow=sqrt(nrow(X)), ncol=sqrt(nrow( X))),
    main="Adjusted Inclusion Probabilities", ylab="y", xlab="x")
  points( X[legacySites,], pch=20, col=1)
}
#tidy
rm( N, n, nLegacy, X, p, legacySites, p2)

MBHdesign documentation built on Sept. 25, 2023, 5:08 p.m.