SimpleGreedy

SimpleGreedy2

<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>
Simple algorithm from paper Friedrich et al. (2015) for integer-valued
optimal allocation in stratified sampling.

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

SimpleGreedy 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>nh</dt>
<dd><ul>
<li>initial allocation (if not given then nh=mh).</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

<ul>
<li><code>SimpleGreedy2()</code>:</li>
</ul>

Functions

<a href='https://lifecycle.r-lib.org/articles/stages.html#experimental'><img src='figures/lifecycle-experimental.svg' alt='[Experimental]' /></a>
Simple algorithm from paper Friedrich et al. (2015) for integer-valued
optimal allocation in stratified sampling.

Integer-valued Optimal Univariate Allocation Under Constraints for Stratified
Sampling — SimpleGreedy

<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>nh</dt>
<dd><ul>
<li>initial allocation (if not given then nh=mh).</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>

SimpleGreedy: Integer-valued Optimal Univariate Allocation Under Constraints for Stratified Sampling

Description

Usage

Value

Arguments

Functions

References