This package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost 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 applying the Bethel-Chromy algorithm to calculate the sampling size satisfying precision 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. Functions for the execution of the genetic algorithm are a modified version of the functions in the 'genalg' package.
library(SamplingStrata)data(swisserrors)data(swissstrata)solution<- optimizeStrata (errors=swisserrors,strata=swissstrata,showPlot=FALSE)# update sampling strata with new strata labelsnewstrata<- updateStrata(swissstrata,solution,writeFiles=FALSE)# update sampling frame with new strata labelsdata(swissframe)framenew<- updateFrame(frame=swissframe,newstrata=newstrata,writeFile=FALSE)samp<- selectSample(framenew,solution$aggr_strata,writeFiles=TRUE)# evaluate the current solutioneval<- evalSolution(frame=framenew,outstrata=solution$aggr_strata,nsampl=100,cens=NULL,writeFiles=FALSE)eval$coeff_varswisserrors