| dual_frame_allocation | R Documentation |
Hartley's (1962, 1974) design for two frames whose union covers the
population: frame A (typically the area frame, complete but expensive) and
frame B (the list, cheap but incomplete). The population splits into the
domains a (A only), ab (both) and b (B only), and the total is
estimated by Y_a + theta * Y_ab(A) + (1 - theta) * Y_ab(B) + Y_b: the
overlap is estimated from both samples and mixed with weight theta.
With simple random sampling in each frame (other designs enter through
design effects) the variance is a sum of two frame terms, so for each
theta the cost-optimal n_A, n_B follow the cost-weighted Neyman
rule; theta is then optimised. theta = 0 is the screening design,
in which overlap units found in the area sample are not used (the list
covers them), the usual practice when the list holds the large producers.
dual_frame_allocation(
domains,
cost_a,
cost_b,
deff_a = 1,
deff_b = 1,
theta = NULL,
target_variance = NULL,
target_cv = NULL,
budget = NULL
)
domains |
Data frame with one row per domain |
cost_a, cost_b |
Cost per sampled unit in frame A and frame B. |
deff_a, deff_b |
Design effects of the two samples relative to simple
random sampling (an area sample of segments or points usually has
|
theta |
|
target_variance, target_cv, budget |
Exactly one: target variance of the
estimated total, target coefficient of variation (relative to the
population total implied by |
The cost per area unit should include the field work: take it from the
routing, for example cost_mean / n of a cost_variance_frontier() at the
sample size under consideration, and iterate once or twice.
A list of class fieldopt_dual_frame: n_a, n_b, theta,
cost, variance, se, cv, total (implied population total),
variance_a, variance_b, overlap_a, overlap_b (expected overlap
units in each sample), bounded, and the inputs.
Hartley, H. O. (1962). Multiple frame surveys. Proceedings of the Social Statistics Section, ASA, 203–206. Hartley, H. O. (1974). Multiple frame methodology and selected applications. Sankhya C, 36, 99–118. Lohr, S. L. and Rao, J. N. K. (2006). Estimation in multiple-frame surveys. JASA, 101(475), 1019–1030.
domains <- data.frame(
domain = c("a", "ab", "b"), size = c(8000, 1500, 500),
mean = c(5, 40, 60), sd = c(6, 30, 50)
)
dual_frame_allocation(domains, cost_a = 200, cost_b = 40, budget = 1e5)
dual_frame_allocation(domains, cost_a = 200, cost_b = 40, theta = "screening", target_cv = 0.05)
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