get.dist

A vector: usually a column in a given data frame

data

My environment my_env has various constants and data that are
used by the get.dist() function

my_env

This function is called at the start of the stratification process where the
best-fit distribution and it parameters are estimated and returned for further
processing towards the computation of stratum boundaries. It basically takes in
the data and fits it with a list of 10 possible distributions and computes
the parameters for all given distributions. It selects the best-fit distribution
to be the one with the lowest AIC

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

get.dist function

<dl><dt>data</dt>
<dd>A vector: usually a column in a given data frame</dd>
<dt>my_env</dt>
<dd>My environment my_env has various constants and data that are
used by the get.dist() function</dd></dl>

Arguments

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

Author

To identify the best-fit distribution of a univariate data — get.dist

<dl>

<dt>data</dt>
<dd>A vector: usually a column in a given data frame</dd>


<dt>my_env</dt>
<dd>My environment my_env has various constants and data that are
used by the get.dist() function</dd>

</dl>

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

get.dist: To identify the best-fit distribution of a univariate data

Description

Usage

Value

Arguments

Author