Once optimal stratification has been obtained, and a new frame has been built by assigning to the units of the old one the new strata labels, it is possible to select a stratified sample from the frame with the simple random sampling without replacement (srswor) method. The result of the execution of "selectSample" function is a dataframe containing the selected units, with their weights (inverse of the probabilities of inclusion). It is possible to output this dataframe in a .csv file. One more .csv file is produced ("sampling_check"), containing coeherence checks between (a) population in frame strata (b) population in optimised strata (c) planned units to be selected in optimised strata (d) actually selected units (e) sum of weights in each stratum

selectSample(frame, outstrata, writeFiles = FALSE,verbatim=TRUE)

Arguments

frame

This is the (mandatory) dataframe containing the sampling frame, as it has been modified by the execution of the "updateFrame" function.

outstrata

This is the (mandatory) dataframe containing the information related to resulting stratification obtained by the execution of "optimizeStrata" function. It should coincide with 'solution$aggr_strata'.

writeFiles

Indicates if at the end of the processing the resulting strata will be outputted in a delimited file. Default is "FALSE".

verbatim

Indicates if information on the drawn sample must be printed or not. Default is "TRUE".

Value

A dataframe containing the sample

Author

Giulio Barcaroli with contribution from Diego Zardetto

Examples

if (FALSE) { library(SamplingStrata) data(swisserrors) data(swissstrata) # optimisation of sampling strata solution <- optimizeStrata ( errors = swisserrors, strata = swissstrata ) # updating sampling strata with new strata labels newstrata <- updateStrata(swissstrata, solution) # updating sampling frame with new strata labels data(swissframe) framenew <- updateFrame(frame=swissframe,newstrata=newstrata) # selection of sample sample <- selectSample(frame=framenew,outstrata=solution$aggr_strata) head(sample) }