R/cvs_hint.R

In barcaroli/R2BEAT: Multistage Sampling Allocation and Sample Selection

#' cvs_hint
#'  
#' @description
#' Function to propose a convenient set of precision constraints,
#' given the different variability of target variables Ys in the strata
#' 
#' @details
#' The function requires in input the 'strata' dataset, plus an initial
#' set of precision constraints. It is suggested to define a 'neutral'
#' set, i.e. equal CVs for all variable, possibly differentiated
#' only with regard to the different domain levels
#'  
#' 
#' @param strata the 'strata' dataset.
#' @param errors the 'errors' dataset containing the current precision constraints.
#' 
#' @return the new 'errors' dataset containing the changed precision constraints
#' 
#' @examples
#' data(beat.example)
#' errors[1,c(2:3)] <- c(0.03,0.03)
#' errors[2,c(2:3)] <- c(0.03,0.03)
#' errors
#' cv <- CVs_hint(strata,errors)
#' cv
CVs_hint <- function (strata, cv) 
{
  ndom <- length(grep("DOM", colnames(strata)))
  nvar <- ncol(cv) - 1
  S <- paste0("S", c(1:nvar))
  M <- paste0("M", c(1:nvar))
  cv_new <- cv
  for (i in c(1:ndom)) {
    eval(parse(text = paste0("vett <- list(as.numeric(as.factor(as.character(strata$DOM",i,"))))")))
    vett <- unlist(vett)
    a <- aggrStrata(strata, vett, dominio = 1, nvar = nvar, censiti = "CENS")
    cvs <- a[, c(S)]/(a[, c(M)]+0.00000000001)
    cv_new[i, c(2:(nvar + 1))] <- cv_new[i, c(2:(nvar + 1))] * apply(cvs, 2, FUN = mean)
  }
  cv_new
}