SamplingBigData-package: Sampling Methods for Big Data

In SamplingBigData: Sampling Methods for Big Data

Description

Select sampling methods for probability samples using large data sets. This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C using efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.

Author(s)

Jonathan Lisic, Anton Grafström

Maintainer: Jonathan Lisic <[e-mail]>

Webpage: https://github.com/jlisic/SamplingBigData

References

Deville, J.-C. and Tillé, Y. (1998). Unequal probability sampling without replacement through a splitting method. Biometrika 85, 89-101.

Grafström, A. (2012). Spatially correlated Poisson sampling. Journal of Statistical Planning and Inference, 142(1), 139-147.

Grafström, A. and Lundström, N.L.P. (2013). Why well spread probability samples are balanced. Open Journal of Statistics, 3(1).

Grafström, A. and Schelin, L. (2014). How to select representative samples. Scandinavian Journal of Statistics.

Grafström, A., Lundström, N.L.P. and Schelin, L. (2012). Spatially balanced sampling through the Pivotal method. Biometrics 68(2), 514-520.

Grafström, A. and Tillé, Y. (2013). Doubly balanced spatial sampling with spreading and restitution of auxiliary totals. Environmetrics, 24(2), 120-131.

Lisic, L. and Cruze, N. (2016). Local Pivotal Methods for Large Surveys. In proceedings, ICES V, Geneva Switzerland 2016.

Examples

# *********************************************************
# check inclusion probabilities
# *********************************************************
set.seed(1234567);
p = c(0.2, 0.25, 0.35, 0.4, 0.5, 0.5, 0.55, 0.65, 0.7, 0.9);
N = length(p);
X = cbind(runif(N),runif(N));
p1 = p2 = p3 = p4 = rep(0,N);
nrs = 1000; # increase for more precision 
for(i in seq(nrs) ){

  # lpm2 kdtree
  s = lpm2_kdtree(p,X);
  p1[s]=p1[s]+1;
  
  # pivotal method 
  s = split_sample(p);
  p2[s]=p2[s]+1;
  
}
print(p);
print(p1/nrs);
print(p2/nrs);

Example output

 [1] 0.20 0.25 0.35 0.40 0.50 0.50 0.55 0.65 0.70 0.90
 [1] 0.206 0.278 0.319 0.386 0.521 0.500 0.567 0.645 0.688 0.890
 [1] 0.188 0.261 0.346 0.367 0.499 0.511 0.545 0.669 0.705 0.909

SamplingBigData documentation built on May 2, 2019, 1:44 p.m.