lcube: Local cube method (Doubly balanced sampling)

In jlisic/BalancedSampling: Balanced and Spatially Balanced Sampling

Description

Select doubly balanced samples with prescribed inclusion probabilities from a finite population. To have a fixed sample size, include the inclusion probabilities as a balancing variable in Xbal and make sure the inclusion probabilities sum to a positive integer. This is a simplified (optimized for speed) implementation of the local cube method (doubly balanced sampling). Landing is done by dropping balancing variables (from rightmost column, so keep inclusion probabilities in first column to guarantee fixed size). Euclidean distance is used in the Xspread space.

Usage

lcube(prob,Xspread,Xbal)	

Arguments

prob	vector of length N with inclusion probabilities
Xspread	matrix of (standardized) auxiliary variables of N rows and q columns
Xbal	matrix of balancing auxiliary variables of N rows and r columns

Value

Returns a vector of selected indexes in 1,2,...,N.

References

Grafstr<c3><b6>m, A. and Till<c3><a9>, Y. (2013). Doubly balanced spatial sampling with spreading and restitution of auxiliary totals. Environmetrics, 24(2), 120-131.

Examples

## 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 = lcube(p,X,cbind(p)); # 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 = lcube(p,X,cbind(p));
  ep[s]=ep[s]+1;
}
print(ep/nrs);


## End(Not run)

jlisic/BalancedSampling documentation built on May 19, 2019, 12:46 p.m.