sb {BalancedSampling} | R Documentation |
Spatial balance
Description
Calculates the spatial balance of a sample.
Usage
sb(prob, x, sample, type = "kdtree2", bucketSize = 10)
sblb(prob, x, sample, type = "kdtree2", bucketSize = 10)
Arguments
prob |
A vector of length N with inclusion probabilities, or an integer > 1. If an integer n, then the sample will be drawn with equal probabilities n / N. |
x |
An N by p matrix of (standardized) auxiliary variables. Squared euclidean distance is used in the |
sample |
A vector of sample indices. |
type |
The method used in finding nearest neighbours.
Must be one of |
bucketSize |
The maximum size of the terminal nodes in the k-d-trees. |
Details
About voronoi and sumofsquares
Value
The balance measure of the provided sample.
Functions
-
sblb()
: Spatial balance using local balance
k-d-trees
The type
s "kdtree" creates k-d-trees with terminal node bucket sizes
according to bucketSize
.
"kdtree0" creates a k-d-tree using a median split on alternating variables.
"kdtree1" creates a k-d-tree using a median split on the largest range.
"kdtree2" creates a k-d-tree using a sliding-midpoint split.
"notree" does a naive search for the nearest neighbour.
References
Friedman, J. H., Bentley, J. L., & Finkel, R. A. (1977). An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software (TOMS), 3(3), 209-226.
Maneewongvatana, S., & Mount, D. M. (1999, December). It’s okay to be skinny, if your friends are fat. In Center for geometric computing 4th annual workshop on computational geometry (Vol. 2, pp. 1-8).
Stevens Jr, D. L., & Olsen, A. R. (2004). Spatially balanced sampling of natural resources. Journal of the American statistical Association, 99(465), 262-278.
Grafström, A., Lundström, N.L.P. & Schelin, L. (2012). Spatially balanced sampling through the Pivotal method. Biometrics 68(2), 514-520.
Prentius, W, & Grafström A. (2023). Manuscript.
See Also
Other measure:
vsb()
Other measure:
vsb()
Examples
## Not run:
set.seed(12345);
N = 500;
n = 70;
prob = rep(n / N, N);
x = matrix(runif(N * 2), ncol = 2);
s = lpm2(prob, x);
b = sb(prob, x, s);
## End(Not run)