R/qsr.R

Defines functions qsr

Documented in qsr

#' @title Income quintile share ratio
#'
#' @author A. Berihuete, C.D. Ramos and M.A. Sordo
#'
#' @description Estimates the  quintile share ratio of an income distribution. It is defined as the ratio of total income  received by the 20 percent of the population with the highest income to that received by the 20 percent of the population with the lowest income.
#'
#' @param dataset a data.frame containing the variables.
#' @param ipuc a character string indicating the variable name of the income per unit of consumption. Default is "ipuc".
#' @param hhcsw a character string indicating the variable name of the household cross-sectional weight. Default is "DB090".
#' @param hhsize a character string indicating the variable name of the household size. Default is "HX040".
#' @param ci a scalar or vector containing the confidence level(s) of the required interval(s). Default does not calculate the confidence interval.
#' @param rep a number to do the confidence interval using boostrap technique.
#' @param verbose logical; if TRUE the confidence interval is plotted.
#'
#' @details It is calculated using the equivalised disposable income. Two types of equivalence scales can be used, the modified OECD scale and the parametric scale of Buhmann et al. ( 1988). The default is the modified OECD scale  (see setupDataset).
#'
#' @return The value of the income quintile share ratio.
#'
#' @references B. Buhmann et al. (1988) Equivalence scales, well-being, inequality and poverty: sensitivity estimates across ten countries using the Luxembourg Income Study (LIS) database, Review of Income and Wealth, 34, 115--142.
#' @references \url{http://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Income_quintile_share_ratio}
#'
#' @examples
#' data(eusilc2)
#' ATdataset <- setupDataset(eusilc2, country = "AT")
#' qsr(ATdataset)
#'
#' @seealso setupDataset
#' @import boot
#' @export

qsr <- function(dataset,
                ipuc = "ipuc", # The income per unit of consumption
                hhcsw = "DB090", # Household cross-sectional weight
                hhsize = "HX040", # Household size
                ci = NULL, rep = 1000, verbose = FALSE){
  dataset <- dataset[order(dataset[,ipuc]), ]
  dataset$wHX040 <- dataset[,hhcsw]*dataset[,hhsize] # household weights taking into account the size of the household

  if(is.null(ci)){
    dataset$acum.wHX040 <- cumsum(dataset$wHX040)
    dataset$abscisa2 <-
      dataset$acum.wHX040/dataset$acum.wHX040[length(dataset$acum.wHX040)]
    A <- dataset[,ipuc]*dataset$wHX040
    uc.S20 <- sum(A[which(dataset$abscisa2 < 0.2)])
    uc.S80 <- sum(A[which(dataset$abscisa2 > 0.8)])
    qsr <- uc.S80/uc.S20
    return(qsr)
  }else{
    qsr3 <- function(dataset, i){
      dataset.boot <- dataset[i,]
      dataset.boot <- dataset.boot[order(dataset.boot[,ipuc]), ]
      dataset.boot$acum.wHX040 <- cumsum(dataset.boot$wHX040)
      dataset.boot$abscisa2 <-
        dataset.boot$acum.wHX040/dataset.boot$acum.wHX040[length(dataset.boot$acum.wHX040)]
      A <- dataset.boot[,ipuc]*dataset.boot$wHX040
      uc.S20 <- sum(A[which(dataset.boot$abscisa2 < 0.2)])
      uc.S80 <- sum(A[which(dataset.boot$abscisa2 > 0.8)])
      uc.S80/uc.S20
    }
    boot.qsr <- boot::boot(dataset, statistic = qsr3, R = rep,
                      sim = "ordinary", stype = "i")
    qsr.ci <- boot::boot.ci(boot.qsr, conf = ci, type = "basic")
    if(verbose == FALSE){
      return(qsr.ci)
    }else{
      plot(boot.qsr)
      summary(qsr.ci)
      return(qsr.ci)
    }
  }
}

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rtip documentation built on April 12, 2018, 5:04 p.m.