rdca_iter: Iterative RDCA Implementation

In stratallo: Optimum Sample Allocation in Stratified Sampling

Iterative RDCA Implementation

Description

Iterative implementation of the Recursive Domain-Controlled Allocation (RDCA) algorithm. Not tested.

Usage

rdca_iter(n, H_counts, N, S, rho, rho2 = NULL, ref_domain = 1L)

Arguments

n	(integerish(1)) total sample size n. Must satisfy ⁠0 < n <= sum(N)⁠.
H_counts	(integerish) strata counts in each domain.
N	(integerish) strata sizes (N_{d,h},\, (d,h) \in \mathcal H).
S	(numeric) standard deviations (S_{d,h},\, (d,h) \in \mathcal H) of surveyed variable in strata.
rho	(numeric) parameters (\rho_d,\, d \in \mathcal D) of the optimization problem.
rho2	(numeric) the square of rho (rho^2), provided to reduce potential loss of precision due to finite-precision arithmetic.
ref_domain	(integerish(1)) reference domain (denoted by j in the thesis).

Examples

H_counts <- c(2, 2, 3)
N <- c(140, 110, 135, 190, 200, 40, 70)
S <- sqrt(c(180, 20, 5, 4, 35, 9, 40))
total <- c(2, 3, 5)
kappa <- c(0.5, 0.2, 0.3)
rho <- total * sqrt(kappa)
(n <- dca_nmax(H_counts, N, S) - 1)

# experimental function (not exported) – examples skipped
## Not run: 
rdca_iter(n, H_counts, N, S, rho)
# 140.0000 103.6139 132.1970 166.4127 195.9701  19.8750  70.0000

## End(Not run)

stratallo documentation built on March 12, 2026, 5:06 p.m.