lpm {BalancedSampling} | R Documentation |
Select spatially balanced samples with prescribed inclusion probabilities from a finite (large) population using a sub-optimal implementation of the local pivotal method. Euclidean distance is used in the x
space.
lpm(prob,x,h)
prob |
vector of length N with inclusion probabilities |
x |
matrix of (standardized) auxiliary variables of N rows and q columns |
h |
positive integer, size of window in the list to search for nearest neighbor |
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).
## 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 h = 100; # size of search window (for finding nearest neighbor) s = lpm(p,X,h); # 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 = lpm(p,X,10); ep[s]=ep[s]+1; } print(ep/nrs); ## End(Not run)