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 labels
newstrata <- 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$aggr_strata,writeFiles=TRUE)
# evaluate the current solution
eval <- evalSolution(frame = framenew,
outstrata =solution$aggr_strata,
nsampl = 100,
cens = NULL,
writeFiles = FALSE)
eval$coeff_var
swisserrors