pop10_mM

<p>Artificial example population with 10 strata defined by strata
sizes and standard deviations of some study variable. Additionally, the
lower and upper bounds have been specified for the sample in strata.</p>

datasets

Functions in this package provide solution to classical problem in
survey methodology - an optimum sample allocation in stratified sampling
schemes. In this context, the optimal allocation is in the classical
Tschuprov-Neyman's sense and it satisfies additional lower or upper bounds
restrictions imposed on sample sizes in strata. There are few different
algorithms available to use, and one them is based on popular sample
allocation method that applies Neyman allocation to recursively reduced set of
strata.
This package also provides the function that computes a solution to the minimum
sample size allocation problem, which is a minor modification of the classical
optimium sample allocation. This problem lies in the determination of a vector
of strata sample sizes that minimizes total sample size, under assumed fixed
level of the pi-estimator's variance. As in the case of the classical optimal
allocation, the problem of minimum sample size allocation can be complemented
by imposing upper bounds constraints on sample sizes in strata.

Wojciech Wojciak

stratallo

Optimum Sample Allocation in Stratified Sampling Schemes

Wojciech Wójciak

Jacek Wesołowski

Robert Wieczorkowski

pop10_mM function

<p>A matrix with 10 rows and 5 columns:
<code>N</code> (strata sizes), <code>S</code> (standard deviations of a
variable under study in strata), <code>m</code> (lower bounds), <code>M</code> (upper bounds).</p>

Format

Example Population with 10 Strata and Lower and Upper Bounds — pop10_mM

Example Population with 10 Strata and Lower and Upper Bounds

pop10_mM: Example Population with 10 Strata and Lower and Upper Bounds

Description

Usage

Arguments

Format