fpia: Optimal Univariate Allocation Under Constraints for...

In stratallo: Optimum Sample Allocation in Stratified Sampling

Optimal Univariate Allocation Under Constraints for Stratified Sampling

Description

Algorithm for optimal allocation in stratified sampling with lower and upper constraints based on fixed point iteration.

Usage

fpia(
  n,
  Ah,
  mh = NULL,
  Mh = NULL,
  lambda0 = NULL,
  maxiter = 100,
  tol = .Machine$double.eps * 1000
)

fpia2(v0, Nh, Sh, mh = NULL, Mh = NULL, lambda0 = NULL, maxiter = 100)

Arguments

n	target sample size for allocation.
Ah	population strata sizes * standard deviations of a given variable in strata.
mh	lower constraints for sample sizes in strata.
Mh	upper constraints for sample sizes in strata.
lambda0	initial parameter 'lambda' (optional).
maxiter	maximal number of iterations for algorithm.
tol	the desired accuracy (convergence tolerance).
v0	upper limit for value of variance which must be attained for computed optimal allocation.
Nh	population strata sizes.
Sh	standard deviations of a given variable in strata.

Value

A vector of optimal allocation sizes, and number of iterations.

Functions

• fpia2():

References

Münnich, R. T., Sachs, E.W. and Wagner, M. (2012) Numerical solution of optimal allocation problems in stratified sampling under box constraints, AStA Advances in Statistical Analysis, 96(3), pp. 435-450. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10182-011-0176-z")}

stratallo documentation built on Nov. 27, 2023, 1:07 a.m.