fpia

fpia2

<a href="https://lifecycle.r-lib.org/articles/stages.html#experimental"><img src="figures/lifecycle-experimental.svg?package=stratallo&version=2.2.1" alt="[Experimental]"></a>
Algorithm for optimal allocation in stratified sampling with lower and upper
constraints based on fixed point iteration.

Functions in this package provide solution to classical problem in
survey methodology - an optimum sample allocation in stratified sampling. In
this context, the optimum allocation is in the classical Tschuprow-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 cost allocation problem, which is a minor modification of the
classical optimum sample allocation. This problem lies in the determination
of a vector of strata sample sizes that minimizes total cost of the survey,
under assumed fixed level of the stratified estimator's variance. As in the
case of the classical optimum allocation, the problem of minimum cost
allocation can be complemented by imposing upper-bounds constraints on sample
sizes in strata.

Wojciech Wojciak

stratallo

Optimum Sample Allocation in Stratified Sampling

Wojciech Wójciak

Jacek Wesołowski

Robert Wieczorkowski

fpia function

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

Arguments

Functions

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
Algorithm for optimal allocation in stratified sampling with lower and upper
constraints based on fixed point iteration.

Optimal Univariate Allocation Under Constraints for Stratified
Sampling — fpia

<dl>

<dt>n</dt>
<dd><ul>
<li>target sample size for allocation.</li>
</ul></dd>


<dt>Ah</dt>
<dd><ul>
<li>population strata sizes * standard deviations of a given variable in strata.</li>
</ul></dd>


<dt>mh</dt>
<dd><ul>
<li>lower constraints for sample sizes in strata.</li>
</ul></dd>


<dt>Mh</dt>
<dd><ul>
<li>upper constraints for sample sizes in strata.</li>
</ul></dd>


<dt>lambda0</dt>
<dd><ul>
<li>initial parameter 'lambda' (optional).</li>
</ul></dd>


<dt>maxiter</dt>
<dd><ul>
<li>maximal number of iterations for algorithm.</li>
</ul></dd>


<dt>tol</dt>
<dd><ul>
<li>the desired accuracy (convergence tolerance).</li>
</ul></dd>


<dt>v0</dt>
<dd><ul>
<li>upper limit for value of variance which must be attained for
computed optimal allocation.</li>
</ul></dd>


<dt>Nh</dt>
<dd><ul>
<li>population strata sizes.</li>
</ul></dd>


<dt>Sh</dt>
<dd><ul>
<li>standard deviations of a given variable in strata.</li>
</ul></dd>

</dl>

fpia: Optimal Univariate Allocation Under Constraints for Stratified Sampling

Description

Usage

Value

Arguments

Functions

References