Allocation-Methods: Algorithms for Exact Optimization Allocation

Description Usage Arguments Value References

Description

Algorithms III and IV from Wright (2017), and classical (unconstrained) Neyman allocation. Algorithm III finds the optimial allocation for a given total sample size n. Algorithm IV samples until the overall variance is smaller than a given v0.

Usage

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algIII(n, N.str, S.str, lo.str = rep(1, length(N.str)), hi.str = rep(Inf,
  length(N.str)), verbose = FALSE)

algIV(v0, N.str, S.str, lo.str = rep(1, length(N.str)), hi.str = rep(Inf,
  length(N.str)), verbose = FALSE)

neyman(n, N.str, S.str)

Arguments

n

Target sample size for Algorithm III (integer)

N.str

Population size for each stratum (integer vector)

S.str

Standard deviation for each stratum (numeric vector)

lo.str

Sample size lower bounds for each stratum (numeric vector)

hi.str

Sample size upper bounds for each stratum (numeric vector)

verbose

Print detailed information for each selection (boolean)

v0

Target variance for Algorithm IV (numeric)

Value

An object which contains the results; the structure depends on allocation method.

References

Tommy Wright (2012). The Equivalence of Neyman Optimum Allocation for Sampling and Equal Proportions for Apportioning the U.S. House of Representatives. The American Statistician, 66, pp.217-224.

Tommy Wright (2017), Exact optimal sample allocation: More efficient than Neyman, Statistics & Probability Letters, 129, pp.50-57.


andrewraim/tommysampling documentation built on May 6, 2018, 5:03 a.m.