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首页> 外文期刊>Australian & New Zealand journal of statistics >stratifyR: An R Package for optimal stratification and sample allocation for univaViate populations
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stratifyR: An R Package for optimal stratification and sample allocation for univaViate populations

机译：STRATIFYR：用于无限种群的最佳分层和样品分配的R包

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摘要

ThisRpackage determines optimal stratification of univariate populations under stratified sampling designs using a parametric-based method. It determines the optimum strata boundaries (OSB), optimum sample sizes (OSS) and multiple other quantities for the study variable,y, using the best-fit probability density function of a study variable available from survey data. The method requires the parameters and other characteristics of the distribution of the study variable to be known, either from available data or from a hypothetical distribution if the data are not available. In the implementation, the problem of determining the OSB is formulated as a mathematical programming problem and solved by using a dynamic programming technique. If the data of the population (i.e. the study variable) are 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 realisations of proxy values ofyfrom past/recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, the package handles stratification for the populations where the study variable follows a continuous distribution: namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Lognormal and Cauchy distributions. In this paper, applications of major functionalities in the package are illustrated with a number of real/simulated as well as some hypothetical populations.
机译：ThisTrpackage使用参数化方法确定分层采样设计下的单变量群的最佳分层。它使用调查数据可获得的研究变量的最佳拟合概率密度函数确定研究变量Y的最佳分层边界（OSB），最佳样本大小（OSS）和多个其他数量。该方法需要从可用数据或假设分布中已知的参数和其他特征，或者如果数据不可用，则可以从假设的分布。在实现中，将确定OSB的问题被制定为数学编程问题，并通过使用动态编程技术来解决。如果验船师可以使用人口的数据（即，研究变量），该方法估计其最佳拟合分布并直接根据奈曼分配下的OSB和OSS。当数据集不可用时，基于假设研究变量y的值可用为FROM /近期调查的代理值的假设实现来进行分层。因此，它需要关于研究变量的某些分布假设。目前，包装处理研究变量遵循连续分布的群体的分层：即帕累托，三角形，右三角形，威布尔，伽玛，指数，均匀，正常，伐木和陶池分布。在本文中，包装中主要功能的应用被展示了许多真正的/模拟以及一些假设群体。

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