Prepare suggestions for optimization with method = "continuous" or "spatial"

This function has to be used only in conjunction with "KmeansSolution2" or with "KmeansSolutionSpatial", i.e. in the case of optimizing with only continuous stratification variables. This function prepares the suggestion for the optimization function in case of continuous variables (i.e. with with "optimStrata" when method = "continuous" or method = "spatial").

prepareSuggestion(kmean = kmean, frame = frame, nstrat = nstrat)

Arguments

kmean

The result of the execution of function 'KmeansSolution2'.

frame

The dataframe containing the information related to each unit in the sampling frame.

nstrat

The vector of number of strata identified as the best for each domain.

Value

A dataframe containing the suggestions

Author

Giulio Barcaroli

Examples

if (FALSE) {
library(SamplingStrata)
data("swissmunicipalities")
swissmunicipalities$id <- c(1:nrow(swissmunicipalities))
swissmunicipalities$dom <- 1
frame <- buildFrameDF(swissmunicipalities,
                      id = "id",
                      domainvalue = "REG",
                      X = c("POPTOT", "HApoly"),
                      Y = c("Surfacesbois", "Airind")
)
ndom <- length(unique(frame$domainvalue))
cv <- as.data.frame(list(
              DOM = rep("DOM1",ndom),
              CV1 = rep(0.1,ndom),
              CV2 = rep(0.1,ndom),
              domainvalue = c(1:ndom)))

# Solution with kmean clustering 
kmean <- KmeansSolution2(frame,model=NULL,errors=cv,nstrata=NA,maxclusters=4)
# Number of strata for each domain
nstrat <- tapply(kmean$suggestions,
                 kmean$domainvalue,
                 FUN=function(x) length(unique(x)))
# Prepare suggestion for optimization step                 
sugg <- prepareSuggestion(kmean = kmean,
                          frame = frame,
                          nstrat = nstrat)
# Optimization
solution <- optimStrata (
  method="continuous",
  errors=cv, 
  framesamp=frame,
  iter = 50,
  pops = 10,
  nStrata = nstrat,
  suggestions = sugg)
}