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.
You can install SamplingStrata from github with:
# install.packages("devtools")devtools::install_github("barcaroli/SamplingStrata")This is a basic example which shows you how to solve a common problem:
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 labels
data(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