lpm2 {BalancedSampling} | R Documentation |
Select spatially balanced samples with prescribed inclusion probabilities from a finite population. Euclidean distance is used in the x
space.
lpm2(prob,x)
prob |
vector of length N with inclusion probabilities |
x |
matrix of (standardized) auxiliary variables of N rows and q columns |
Returns a vector of selected indexes in 1,2,...,N. If the inclusion probabilities sum to n, where n is integer, then the sample size is fixed (n).
Grafström, A., Lundström, N.L.P. and Schelin, L. (2012). Spatially balanced sampling through the Pivotal method. Biometrics 68(2), 514-520.
## Not run: # Example 1 set.seed(12345); N = 1000; # population size n = 100; # sample size p = rep(n/N,N); # inclusion probabilities X = cbind(runif(N),runif(N)); # matrix of auxiliary variables s = lpm2(p,X); # select sample plot(X[,1],X[,2]); # plot population points(X[s,1],X[s,2], pch=19); # plot sample # Example 2 # check inclusion probabilities set.seed(12345); p = c(0.2, 0.25, 0.35, 0.4, 0.5, 0.5, 0.55, 0.65, 0.7, 0.9); # prescribed inclusion probabilities N = length(p); # population size X = cbind(runif(N),runif(N)); # some artificial auxiliary variables ep = rep(0,N); # empirical inclusion probabilities nrs = 10000; # repetitions for(i in 1:nrs){ s = lpm2(p,X); ep[s]=ep[s]+1; } print(ep/nrs); ## End(Not run)