## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----eval = FALSE-------------------------------------------------------------
# library(BuSuCo)
# 
# # Generate synthetic business data
# DATA <- Gen.Data(
#   N = 1000,
#   periods = 5,
#   D = 2,
#   D.probs = c(0.8, 0.2),
#   H = 3,
#   borders = c(100, 500),
#   corr.B.U = 0.75
# )

## ----eval = FALSE-------------------------------------------------------------
# # Define which enterprises are relevant for each survey
# Sur_rel <- Survey.relevance(
#   DATA,
#   periods = 5,
#   years = c(1, 2),
#   domains.survey = list(1:2, 1),
#   ge.or.less = c("ge", "l"),
#   TO.or.EM = c("TO", "EM"),
#   size.class = c(0, 5)
# )

## ----eval = FALSE-------------------------------------------------------------
# # Stratify the population
# strat <- Stratify(
#   DATA,
#   Survey.relevant = Sur_rel,
#   periods = 5,
#   years = c(1, 2),
#   TO.or.EM = c("TO", "EM"),
#   strata.TO = c(500),
#   strata.EM = c(10)
# )

## ----eval = FALSE-------------------------------------------------------------
# # Apply Dutch BSC algorithm for period 1
# result <- Dutch.BSC(
#   DATA = DATA,
#   period = 1,
#   sf = c(0.2, 0.5),        # Sampling fractions
#   rf = c(0.5, 1),          # Rotation fractions
#   years = c(1, 2),         # Survey periodicity
#   chi_S = c(0.5, 2),       # Response burden in hours
#   survey.prio = 1:2,       # Survey priority order
#   burden.periods = 5,
#   seed = 123,
#   Survey.relevant = Sur_rel,
#   stratification = strat
# )
# 
# # Use updated DATA for next period
# DATA <- result$DATA
# in_sample <- result$in.sample