dev/OPAG_refactor.R
In timriffe/DemoTools: Standardize, Evaluate, and Adjust Demographic Data
# Author: Tim Riffe
# Dec 27, 2017.. hahaha and Dec 27 2018 too!
# OPAG fresh from scratch, using mostly verbal description of how it should work.
# may or may not have a standard to test against, that will come later if so.
# ----------------------------------
# standard pop is a vector here. Could come from anywhere...
# make utilities to produce standards.
# assume single ages. can always graduate or group as required.
#' redistripute an open age group count over higher ages proportional to an arbitrary standard
#' @description This method could be useful whenever a reasonable standard is available. At present the standard must be supplied by the user.
#' @details In this implementation both the original population counts and the standard must be in single ages.
#' @param Pop numeric vector of population counts
#' @param Age integer vector of single age lower bounds
#' @param OAnow integer. The lower age bound above which counts will be redistributed
#' @param StPop numeric vector of standard population counts
#' @param StAge integer vector of single age lower bounds for the standard population
#' @param OAnew integer. The desired new open age, must be no higher than \code{max(StAge)}.
#' @export
#' @references
#' \insertRef{PAS}{DemoTools}
#' @examples
#' Pop <- c(38129,38382,38824,39275,39500,37304,35152,
#' 34061,33911,32875,31599,30376,29822,29691,28765,
#' 28695,28917,28203,29209,30316,29062,26977,26577,
#' 27727,28599,30513,31774,32347,34093,33736,32085,
#' 30807,28279,26873,25612,23503,22207,21388,20122,
#' 18014,15626,15006,14158,11195,7931,7640,9053,
#' 13276,17226,18918,17697,18424,17723,16706,14410,
#' 13342,14787,15183,15727,16045,14777,14267,13102,
#' 10866,9311,6933,5030,3785,3551,2848,3080,
#' 2874,2368,2681,3165,3010,3009,2721,2705,
#' 2492,2244,1971,1644,1565,1307,5027)
#' Age <- 0:85
#' # standard pop taken from ages 55+
#' StPop <- c(6258,6177,6089,5995,5894,5787,5672,5552,
#' 5423,5286,5140,4985,4824,4652,4477,4293,
#' 4107,3912,3712,3502,3282,3055,2823,2591,
#' 2360,2138,1921,1710,1502,1297,1098,910,
#' 741,592,463,353,265,192,137,95,
#' 63,42,26,17,9,13)
#'
#' StAge <- 55:100
#'
#' PopExtended <- OPAG_simple(
#' Pop = Pop,
#' Age = Age,
#' StPop = StPop,
#' StAge = StAge)
#'
#' \dontrun{
#' plot(Age, Pop, type = 'l',xlim=c(80,100),ylim=c(0,1e4))
#' lines(0:100, PopExtended, col = "red", lty = 2)
#' }
#' stopifnot((sum(PopExtended[86:101]) - Pop[86]) == 0)
OPAG_simple <- function(Pop, Age, OAnow = max(Age), StPop, StAge, OAnew = max(StAge)){
# assume single
stopifnot(is_single(Age))
stopifnot(is_single(StAge))
# OAG can be less than or equal to max age
stopifnot(OAnow %in% Age)
# age and pop vectors must match lengths, assume ordered
stopifnot(length(Pop) == length(Age))
# age concordance
#stopifnot(all(Age %in% StAge))
# group pop down to OAG
Pop <- groupOAG(Pop, Age, OAnow)
StPop <- groupOAG(StPop, StAge, OAnew)
# even up lengths
N <- length(Pop)
Age <- Age[1:N]
OAtot <- Pop[N]
# same for standard
StN <- length(StPop)
StAge <- StAge[1:StN]
# make stadnard distribution.
standard <- rescale.vector(StPop[StAge >= OAnow], scale = 1)
# redistribute OAG
PopUpper <- OAtot * standard
# keep lower ages of Pop
PopLower <- Pop[1:(N-1)]
# graft, name, and return
out <- c(PopLower, PopUpper)
Ageout <- sort(unique(c(Age,StAge)))
names(out) <- Ageout
out
}
## Author: sean
## Edited 9-Dec-2017, Tim Riffe
################################################################################
##
#
##' Distributes the population of an open-ended age group at age 65 into 5-year age groups with open-ended age group at age 80.
##' @description This follows the processes set forward in the OPAG PAS spreadsheet to distribute an open-ended age group through age 65 out to an open-ended age group at age 80.
##'
##' @param Value numeric. A vector of demographic population counts organized into 5-year age groups with one column for s and one column for fes.
##' @param Age numeric vector of ages corresponding to the lower integer bound of the 5-year age ranges.
##' @param BirthLE numeric. A value for the life expectancy at birth for the population with one column for s and one for fes.
##' @param LTCode string. A description of the type of life table to use for the distribution. Default "Coale-Demeny West".
##' @param LxLT numeric. A vector of Lx values from a life table that describes the population distribution at the desired age intervals with one column for s and one for fes. This must be structured in 5-year age groups up to age 80. Default is \code{NULL}.
##' @param FinalLE numeric. A vector of LE values for the final age group with one column for s and one for fes. Default is \code{NULL}.
#
##' @details
##' @return a vector of the age distribution of the smoothed population in 5 year intervals up to and including the new open-ended age group at age 80.
##'
##' @export
##' @author Sean Fennel
#
#opag <- function(Value,
# Age,
# e0,
# LTCode = "coale-demeny west",
# LxLT = NULL,
# FinalLE = NULL,
# Sex = "M"){
# LTCode <- tolower(LTCode)
# #Set up the Lx values of the life table.
# if(LTCode == "coale-demeny west"){
# #Look up the referenced life table.
# modelLT <- getModelLT(LTCode, Sex)
# ex <- modelLT$ex
# maxlevel <- ncol(ex)
# nLx <- modelLT$nLx
# #Find the largest level of the life table that is less than the given LE.
# LevelLow <- max(which(ex[,1] < e0))
#
# #The OPAG spreadsheet only uses every other column (the odd columns) of Coale-Demeny
# if(LevelLow %% 2 == 0){
# LevelLow <- LevelLow - 1
# }
#
# LevelHigh <- LevelLow + 2
#
# #Interpolate between the Lx and e0 values for the levels
# LxLTLow <- nLx[LevelLow,]*100000
# LxLTHigh <- nLx[LevelHigh,]*100000
#
# E0Low <- ex[LevelLow,1]
#
# E0High <- ex[LevelHigh,1]
#
# FactorLow <- (E0High - e0) / (E0High - E0Low)
# FactorHigh <- 1 - FactorLow
#
# LxLTInit <- LxLTLow*FactorLow[1,] + LxLTHigh*FactorHigh[1,]
# E80 <- ex[LevelLow, 18]*FactorLow + ex[LevelHigh, 18]*FactorHigh
#
# LxLTInit2 <- c(LxLTInit[1:(length(Value[,1]))], sum(LxLTInit[(length(Value[,1])+1):length(LxLTInit)]))
#
# LxLTInit2 <- convertSplitTo5Year(LxLTInit)
#
#
# LxLT <- c(LxLTInit2[1:16,], sum(LxLTInit2[17:length(LxLTInit2[,1]),1]))
#
# #Bring things together
# LT5Lx <- data.frame(LxLT)
# FinalLE <- data.frame(E80)
#
# }
# else if(LTCode == "Empirical"){
# LT5Lx <- LxLT
# }
#
# #set up different life tables for the calculations
# LTLxPlus5 <- LT5Lx[-1,]
# LT5LxShort <- LT5Lx[0:(length(LT5Lx[,1])-1),]
# LT10Lx <- LT5LxShort + LTLxPlus5
# LT10Lx <- LT10Lx[c(FALSE, TRUE),]
#
# #set up the different age structures for the calculations below (xPlus10, x)
# Value1Px <- splitToSingleAges(Value[,1], Age)
# Value10Px <- groupAges(Value1Px, Age = seq(0,max(Age)+4, by = 1), N=10, shiftdown = 5)
# Value10Px <- Value10Px
#
# #Estimate the intrinsic growth rate.
# r <- (log(Value10Px[6] / LT10Lx[6]) - log(Value10Px[5] / LT10Lx[5])) / 10
#
# #Preliminary 5-year age distribution
# BaseValue45 <- Value[10,]
# BaseLxValue45 <- LT5Lx[10,]
# AgeOffset <- seq(0, 80, by = 5) - 45
# PrelimValues <- (BaseValue45[, 1])*(LT5Lx[, 1]/BaseLxValue45[, 1])*exp(r[, 1] * AgeOffset)
#
# #Update the preliminary open-ended age group.
# PrelimValues[length(PrelimValues)] <- ((BaseValue45[, 1]) * (LT5Lx[length(LT5Lx[, 1]), 1] / BaseLxValue45[,1])* exp(r[, 1] * (AgeOffset[length(AgeOffset)] + 0.6 * E80 + 0.92)))[[1]]
#
#
# #Distribute preliminary based on proportion of actual totals.
# OpenEnd <- round(PrelimValues[length(Value[,1]):(length(PrelimValues))]*(Value[length(Value[,1]),1] / sum(PrelimValues[length(Value[,1]):(length(PrelimValues))])))
#
# #Create final data frame
# FinalOpenEnd <- data.frame(OpenEnd)
#
# OutputFrame <- rbind(Value[0:(length(Value[,1])-1),], FinalOpenEnd)
#
# return(OutputFrame)
#}
timriffe/DemoTools documentation built on Jan. 28, 2024, 5:13 a.m.
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# Author: Tim Riffe
# Dec 27, 2017.. hahaha and Dec 27 2018 too!
# OPAG fresh from scratch, using mostly verbal description of how it should work.
# may or may not have a standard to test against, that will come later if so.
# ----------------------------------
# standard pop is a vector here. Could come from anywhere...
# make utilities to produce standards.
# assume single ages. can always graduate or group as required.
#' redistripute an open age group count over higher ages proportional to an arbitrary standard
#' @description This method could be useful whenever a reasonable standard is available. At present the standard must be supplied by the user.
#' @details In this implementation both the original population counts and the standard must be in single ages.
#' @param Pop numeric vector of population counts
#' @param Age integer vector of single age lower bounds
#' @param OAnow integer. The lower age bound above which counts will be redistributed
#' @param StPop numeric vector of standard population counts
#' @param StAge integer vector of single age lower bounds for the standard population
#' @param OAnew integer. The desired new open age, must be no higher than \code{max(StAge)}.
#' @export
#' @references
#' \insertRef{PAS}{DemoTools}
#' @examples
#' Pop <- c(38129,38382,38824,39275,39500,37304,35152,
#' 34061,33911,32875,31599,30376,29822,29691,28765,
#' 28695,28917,28203,29209,30316,29062,26977,26577,
#' 27727,28599,30513,31774,32347,34093,33736,32085,
#' 30807,28279,26873,25612,23503,22207,21388,20122,
#' 18014,15626,15006,14158,11195,7931,7640,9053,
#' 13276,17226,18918,17697,18424,17723,16706,14410,
#' 13342,14787,15183,15727,16045,14777,14267,13102,
#' 10866,9311,6933,5030,3785,3551,2848,3080,
#' 2874,2368,2681,3165,3010,3009,2721,2705,
#' 2492,2244,1971,1644,1565,1307,5027)
#' Age <- 0:85
#' # standard pop taken from ages 55+
#' StPop <- c(6258,6177,6089,5995,5894,5787,5672,5552,
#' 5423,5286,5140,4985,4824,4652,4477,4293,
#' 4107,3912,3712,3502,3282,3055,2823,2591,
#' 2360,2138,1921,1710,1502,1297,1098,910,
#' 741,592,463,353,265,192,137,95,
#' 63,42,26,17,9,13)
#'
#' StAge <- 55:100
#'
#' PopExtended <- OPAG_simple(
#' Pop = Pop,
#' Age = Age,
#' StPop = StPop,
#' StAge = StAge)
#'
#' \dontrun{
#' plot(Age, Pop, type = 'l',xlim=c(80,100),ylim=c(0,1e4))
#' lines(0:100, PopExtended, col = "red", lty = 2)
#' }
#' stopifnot((sum(PopExtended[86:101]) - Pop[86]) == 0)
OPAG_simple <- function(Pop, Age, OAnow = max(Age), StPop, StAge, OAnew = max(StAge)){
# assume single
stopifnot(is_single(Age))
stopifnot(is_single(StAge))
# OAG can be less than or equal to max age
stopifnot(OAnow %in% Age)
# age and pop vectors must match lengths, assume ordered
stopifnot(length(Pop) == length(Age))
# age concordance
#stopifnot(all(Age %in% StAge))
# group pop down to OAG
Pop <- groupOAG(Pop, Age, OAnow)
StPop <- groupOAG(StPop, StAge, OAnew)
# even up lengths
N <- length(Pop)
Age <- Age[1:N]
OAtot <- Pop[N]
# same for standard
StN <- length(StPop)
StAge <- StAge[1:StN]
# make stadnard distribution.
standard <- rescale.vector(StPop[StAge >= OAnow], scale = 1)
# redistribute OAG
PopUpper <- OAtot * standard
# keep lower ages of Pop
PopLower <- Pop[1:(N-1)]
# graft, name, and return
out <- c(PopLower, PopUpper)
Ageout <- sort(unique(c(Age,StAge)))
names(out) <- Ageout
out
}
## Author: sean
## Edited 9-Dec-2017, Tim Riffe
################################################################################
##
#
##' Distributes the population of an open-ended age group at age 65 into 5-year age groups with open-ended age group at age 80.
##' @description This follows the processes set forward in the OPAG PAS spreadsheet to distribute an open-ended age group through age 65 out to an open-ended age group at age 80.
##'
##' @param Value numeric. A vector of demographic population counts organized into 5-year age groups with one column for s and one column for fes.
##' @param Age numeric vector of ages corresponding to the lower integer bound of the 5-year age ranges.
##' @param BirthLE numeric. A value for the life expectancy at birth for the population with one column for s and one for fes.
##' @param LTCode string. A description of the type of life table to use for the distribution. Default "Coale-Demeny West".
##' @param LxLT numeric. A vector of Lx values from a life table that describes the population distribution at the desired age intervals with one column for s and one for fes. This must be structured in 5-year age groups up to age 80. Default is \code{NULL}.
##' @param FinalLE numeric. A vector of LE values for the final age group with one column for s and one for fes. Default is \code{NULL}.
#
##' @details
##' @return a vector of the age distribution of the smoothed population in 5 year intervals up to and including the new open-ended age group at age 80.
##'
##' @export
##' @author Sean Fennel
#
#opag <- function(Value,
# Age,
# e0,
# LTCode = "coale-demeny west",
# LxLT = NULL,
# FinalLE = NULL,
# Sex = "M"){
# LTCode <- tolower(LTCode)
# #Set up the Lx values of the life table.
# if(LTCode == "coale-demeny west"){
# #Look up the referenced life table.
# modelLT <- getModelLT(LTCode, Sex)
# ex <- modelLT$ex
# maxlevel <- ncol(ex)
# nLx <- modelLT$nLx
# #Find the largest level of the life table that is less than the given LE.
# LevelLow <- max(which(ex[,1] < e0))
#
# #The OPAG spreadsheet only uses every other column (the odd columns) of Coale-Demeny
# if(LevelLow %% 2 == 0){
# LevelLow <- LevelLow - 1
# }
#
# LevelHigh <- LevelLow + 2
#
# #Interpolate between the Lx and e0 values for the levels
# LxLTLow <- nLx[LevelLow,]*100000
# LxLTHigh <- nLx[LevelHigh,]*100000
#
# E0Low <- ex[LevelLow,1]
#
# E0High <- ex[LevelHigh,1]
#
# FactorLow <- (E0High - e0) / (E0High - E0Low)
# FactorHigh <- 1 - FactorLow
#
# LxLTInit <- LxLTLow*FactorLow[1,] + LxLTHigh*FactorHigh[1,]
# E80 <- ex[LevelLow, 18]*FactorLow + ex[LevelHigh, 18]*FactorHigh
#
# LxLTInit2 <- c(LxLTInit[1:(length(Value[,1]))], sum(LxLTInit[(length(Value[,1])+1):length(LxLTInit)]))
#
# LxLTInit2 <- convertSplitTo5Year(LxLTInit)
#
#
# LxLT <- c(LxLTInit2[1:16,], sum(LxLTInit2[17:length(LxLTInit2[,1]),1]))
#
# #Bring things together
# LT5Lx <- data.frame(LxLT)
# FinalLE <- data.frame(E80)
#
# }
# else if(LTCode == "Empirical"){
# LT5Lx <- LxLT
# }
#
# #set up different life tables for the calculations
# LTLxPlus5 <- LT5Lx[-1,]
# LT5LxShort <- LT5Lx[0:(length(LT5Lx[,1])-1),]
# LT10Lx <- LT5LxShort + LTLxPlus5
# LT10Lx <- LT10Lx[c(FALSE, TRUE),]
#
# #set up the different age structures for the calculations below (xPlus10, x)
# Value1Px <- splitToSingleAges(Value[,1], Age)
# Value10Px <- groupAges(Value1Px, Age = seq(0,max(Age)+4, by = 1), N=10, shiftdown = 5)
# Value10Px <- Value10Px
#
# #Estimate the intrinsic growth rate.
# r <- (log(Value10Px[6] / LT10Lx[6]) - log(Value10Px[5] / LT10Lx[5])) / 10
#
# #Preliminary 5-year age distribution
# BaseValue45 <- Value[10,]
# BaseLxValue45 <- LT5Lx[10,]
# AgeOffset <- seq(0, 80, by = 5) - 45
# PrelimValues <- (BaseValue45[, 1])*(LT5Lx[, 1]/BaseLxValue45[, 1])*exp(r[, 1] * AgeOffset)
#
# #Update the preliminary open-ended age group.
# PrelimValues[length(PrelimValues)] <- ((BaseValue45[, 1]) * (LT5Lx[length(LT5Lx[, 1]), 1] / BaseLxValue45[,1])* exp(r[, 1] * (AgeOffset[length(AgeOffset)] + 0.6 * E80 + 0.92)))[[1]]
#
#
# #Distribute preliminary based on proportion of actual totals.
# OpenEnd <- round(PrelimValues[length(Value[,1]):(length(PrelimValues))]*(Value[length(Value[,1]),1] / sum(PrelimValues[length(Value[,1]):(length(PrelimValues))])))
#
# #Create final data frame
# FinalOpenEnd <- data.frame(OpenEnd)
#
# OutputFrame <- rbind(Value[0:(length(Value[,1])-1),], FinalOpenEnd)
#
# return(OutputFrame)
#}
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