SamplingStrata: Optimal stratification of sampling frames for multipurpose sampling surveys

In the field of sampling design (in particular for stratified sampling), this package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample size under the condition to satisfy precision constraints in a multivariate and multidomain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated by calculating (using the Bethel-Chromy algorithm) the sampling size satisfying accuracy constraints on the target estimates. Functions in the package allows to: (a) analyse the obtained results of the optimisation step; (b) assign the new strata labels to the sampling frame; (c) select a sample from the new frame accordingly to the best allocation. There is also a function that allows to build the most important input to the optimisation step, i.e. the "strata" dataframe, containing information (means and standard errors) regarding the distributions of the target variables in the different strata, using the sampling frame or using data from previous rounds of the same survey. As auxiliary variables that concur to the stratification can be only of the categorical type, the package offers a function ("var.bin") that allows to transform continuous variables into categorical ordinal ones by applying a k-means univariate clustering method. Functions for the execution of the genetic algorithm are a modified version of the functions in the "genalg" package.

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