# ----------------------------------------
# Authors: Andreas Alfons and Josef Holzer
# Vienna University of Technology
# ----------------------------------------
#' Fit income distribution models with the Pareto distribution
#'
#' Fit a Pareto distribution to the upper tail of income data. Since a
#' theoretical distribution is used for the upper tail, this is a semiparametric
#' approach.
#'
#' The arguments \code{k} and \code{x0} of course correspond with each other.
#' If \code{k} is supplied, the threshold \code{x0} is estimated with the \eqn{n
#' - k} largest value in \code{x}, where \eqn{n} is the number of observations.
#' On the other hand, if the threshold \code{x0} is supplied, \code{k} is given
#' by the number of observations in \code{x} larger than \code{x0}. Therefore,
#' either \code{k} or \code{x0} needs to be supplied. If both are supplied,
#' only \code{k} is used (mainly for back compatibility).
#'
#' The function supplied to \code{method} should take a numeric vector (the
#' observations) as its first argument. If \code{k} is supplied, it will be
#' passed on (in this case, the function is required to have an argument called
#' \code{k}). Similarly, if the threshold \code{x0} is supplied, it will be
#' passed on (in this case, the function is required to have an argument called
#' \code{x0}). As above, only \code{k} is passed on if both are supplied. If
#' the function specified by \code{method} can handle sample weights, the
#' corresponding argument should be called \code{w}. Additional arguments are
#' passed via the \dots{} argument.
#'
#' @param x a numeric vector.
#' @param k the number of observations in the upper tail to which the Pareto
#' distribution is fitted.
#' @param x0 the threshold (scale parameter) above which the Pareto distribution
#' is fitted.
#' @param method either a function or a character string specifying the function
#' to be used to estimate the shape parameter of the Pareto distibution, such as
#' \code{\link{thetaPDC}} (the default). See \dQuote{Details} for requirements
#' for such a function and \dQuote{See also} for available functions.
#' @param groups an optional vector or factor specifying groups of elements of
#' \code{x} (e.g., households). If supplied, each group of observations is
#' expected to have the same value in \code{x} (e.g., household income). Only
#' the values of every first group member to appear are used for fitting the
#' Pareto distribution. For each group above the threshold, every group member
#' is assigned the same value.
#' @param w an optional numeric vector giving sample weights.
#' @param \dots addtional arguments to be passed to the specified method.
#'
#' @return A numeric vector with a Pareto distribution fit to the upper tail.
#'
#' @note The arguments \code{x0} for the threshold (scale parameter) of the
#' Pareto distribution and \code{w} for sample weights were introduced in
#' version 0.2. This results in slightly different behavior regarding the
#' function calls to \code{method} compared to prior versions.
#'
#' @author Andreas Alfons and Josef Holzer
#'
#' @seealso \code{\link{paretoTail}}, \code{\link{replaceTail}}
#'
#' \code{\link{thetaPDC}}, \code{\link{thetaWML}}, \code{\link{thetaHill}},
#' \code{\link{thetaISE}}, \code{\link{thetaLS}}, \code{\link{thetaMoment}},
#' \code{\link{thetaQQ}}, \code{\link{thetaTM}}
#'
#' @keywords manip
#'
#' @examples
#' data(eusilc)
#'
#'
#' ## gini coefficient without Pareto tail modeling
#' gini("eqIncome", weights = "rb050", data = eusilc)
#'
#'
#' ## gini coefficient with Pareto tail modeling
#'
#' # using number of observations in tail
#' eqIncome <- fitPareto(eusilc$eqIncome, k = 175,
#' w = eusilc$db090, groups = eusilc$db030)
#' gini(eqIncome, weights = eusilc$rb050)
#'
#' # using threshold
#' eqIncome <- fitPareto(eusilc$eqIncome, x0 = 44150,
#' w = eusilc$db090, groups = eusilc$db030)
#' gini(eqIncome, weights = eusilc$rb050)
#'
#' @importFrom stats optimize runif uniroot
#' @export
fitPareto <- function(x, k = NULL, x0 = NULL,
method = "thetaPDC", groups = NULL, w = NULL, ...) {
## initializations
if(!is.numeric(x) || length(x) == 0) stop("'x' must be a numeric vector")
haveK <- !is.null(k)
if(haveK) { # if 'k' is supplied, it is always used
if(!is.numeric(k) || length(k) == 0 || k[1] < 1) {
stop("'k' must be a positive integer")
} else k <- k[1]
} else if(!is.null(x0)) { # otherwise 'x0' (threshold) is used
if(!is.numeric(x0) || length(x0) == 0) stop("'x0' must be numeric")
else x0 <- x0[1]
} else stop("either 'k' or 'x0' must be supplied")
nam <- argNames(method)
useW <- !is.null(w) && ("w" %in% nam)
if(useW && (!is.numeric(w) || length(w) != length(x))) {
stop("'w' must be numeric vector of the same length as 'x'")
}
haveGroups <- !is.null(groups)
if(haveGroups) {
if(!is.vector(groups) && !is.factor(groups)) {
stop("'groups' must be a vector or factor")
}
if(length(groups) != length(x)) {
stop("'groups' must have the same length as 'x'")
}
if(any(is.na(groups))) stop("'groups' contains missing values")
unique <- !duplicated(groups)
values <- x[unique]
if(useW) w <- w[unique]
} else values <- x
## check for missing values
indices <- 1:length(values)
if(any(i <- is.na(values))) indices <- indices[!i]
## order of observed values
order <- order(values[indices])
indicesSorted <- indices[order] # indices of sorted vector
n <- length(indicesSorted)
## start constructing call to 'method' for estimation of shape parameter
dots <- list(values[indices], ...)
if(haveK) { # 'k' is supplied, threshold is determined
if(k >= n) stop("'k' must be smaller than the number of observed values")
x0 <- values[indicesSorted[n-k]] # threshold (scale parameter)
dots$k <- k # 'method' is expected to have 'k' as argument
} else { # 'k' is not supplied, it is determined using threshold
if(x0 >= values[indicesSorted[n]]) { # compare to sorted values
stop("'x0' must be smaller than the largest value")
}
k <- length(which(values[indices] > x0)) # number of observations in tail
dots$x0 <- x0 # 'method' is expected to have threshold 'x0' as argument
}
## estimate shape parameter
if(useW) dots$w <- w[indices]
theta <- do.call(method, dots)
## fit Pareto distribution
valuesPareto <- x0/runif(k)^(1/theta)
values[indicesSorted[(n-k+1):n]] <- sort(valuesPareto)
## return values
if(haveGroups) {
groups <- as.character(groups)
names(values) <- groups[unique]
values <- values[groups]
names(values) <- names(x)
}
values
}
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