distr.optim

A numeric: 10e-3 when k=1 and 10e-5 for k&gt;=2

incf

A numeric: index to access minimum elements in the matrix

minYk

A numeric: index to access maximum elements in the matrix

maxYk

isFirstRun

My environment my_env has various constants and calculations stored
from earlier opeartions through various other functions

my_env

This function uses the Dynamic Programming (DP) solution procedure in solving the
objective function for the univariate stratification problem. It calculates
the objective function values using the brute-force algorithm and stores those
values in the matrices and keeps a copy in my_env so that a global minimum
could be obtained.

The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) <doi:10.1177/0008068320020518> and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674> in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.

Karuna G. Reddy

stratifyR

Optimal Stratification of Univariate Populations

M. G. M. Khan

distr.optim function

<dl><dt>k</dt>
<dd>A numeric: number of strata</dd>
<dt>n</dt>
<dd>A numeric: is the distance*1000</dd>
<dt>incf</dt>
<dd>A numeric: 10e-3 when k=1 and 10e-5 for k&gt;=2</dd>
<dt>minYk</dt>
<dd>A numeric: index to access minimum elements in the matrix</dd>
<dt>maxYk</dt>
<dd>A numeric: index to access maximum elements in the matrix</dd>
<dt>isFirstRun</dt>
<dd>A boolean: TRUE/FALSE parameter</dd>
<dt>my_env</dt>
<dd>My environment my_env has various constants and calculations stored
from earlier opeartions through various other functions</dd></dl>

Arguments

Karuna Reddy &lt;karuna.reddy@usp.ac.fj&gt; MGM Khan &lt;khan_mg@usp.ac.fj&gt;

Author

To implement the Dynamic Programming (DP) solution procedure on the stratification
problem presented in the form of a Mathematical Programming Problem (MPP) — distr.optim

<dl>

<dt>k</dt>
<dd>A numeric: number of strata</dd>


<dt>n</dt>
<dd>A numeric: is the distance*1000</dd>


<dt>incf</dt>
<dd>A numeric: 10e-3 when k=1 and 10e-5 for k&gt;=2</dd>


<dt>minYk</dt>
<dd>A numeric: index to access minimum elements in the matrix</dd>


<dt>maxYk</dt>
<dd>A numeric: index to access maximum elements in the matrix</dd>


<dt>isFirstRun</dt>
<dd>A boolean: TRUE/FALSE parameter</dd>


<dt>my_env</dt>
<dd>My environment my_env has various constants and calculations stored
from earlier opeartions through various other functions</dd>

</dl>

Karuna Reddy &lt;karuna.reddy@usp.ac.fj&gt; MGM Khan &lt;khan_mg@usp.ac.fj&gt;

To implement the Dynamic Programming (DP) solution procedure on the stratification
problem presented in the form of a Mathematical Programming Problem (MPP)

distr.optim: To implement the Dynamic Programming (DP) solution procedure on the stratification problem presented in the form of a Mathematical Programming Problem (MPP)

Description

Usage

Value

Arguments

Author