R/AGEINT.R
In timriffe/DemoTools: Standardize, Evaluate, and Adjust Demographic Data
Defines functions
interp
interpolatePop
Documented in
interp
interpolatePop
# Author: sean
# edited by TR 9-Dec-2017 & 28 Feb-2018
###############################################################################
#' Interpolate between two population age distributions.
#' @description The interpolation is done by age (not cohort) using a linear or exponential function. This comes from the PAS spreadsheet called ADJINT.
#' @param Pop1 numeric. A vector of demographic counts by age at time 1 (earlier date).
#' @param Pop2 numeric. A vector of demographic counts by age at time 2 (later date).
#' @param Date1 date. The date corresponding to the population age distribution at time 1. See details for ways to express it.
#' @param Date2 date. The date corresponding to the population age distribution at time 2. See details for ways to express it.
#' @param DesiredDate date. The desired date of the output population age distribution. See details for ways to express it.
#' @param method string. The method to use for the interpolation, either "linear" or "exponential". Default "linear".
#' @param roundoutput logical. Whether or not to return integers. Default \code{FALSE}.
#' @details The age group structure of the output is the same as that of the input. Ideally, \code{DesiredDate} should be between the Date1 and Date2.
#' Dates can be given in three ways 1) a \code{Date} class object, 2) an unambiguous character string in the format \code{"YYYY-MM-DD"},
#' or 3) as a decimal date consisting in the year plus the fraction of the year passed as of the given date.
#' @return A vector of the interpolated population for the requested date.
#' @author Sean Fennel
#' @references
#' \insertRef{PAS}{DemoTools}
#' @export
#'
#' @examples
#' # YYYY-MM-DD dates as character
#' EarlyDate <- "1980-04-07"
#' LaterDate <- "1990-10-06"
#' DesiredDate <- "1985-07-01"
#'
#' interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate)
#' interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate, method = "exponential")
#' \dontrun{
#'plot(popA_earlier, t ='l', col='red',
#' ylim = c(400,1220000),
#' ylab = 'The counts', xlab = 'Age groups')
#'
#'lines(interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate),
#' t = 'l', col='black')
#'
#'lines(popA_later,
#' t = 'l', col='blue')
#'
#'legend(12,1000000,
#' legend = c('Pop in 1980-04-07',
#' 'Pop in 1985-07-01',
#' 'Pop in 1990-10-06'),
#' col=c('red','black','blue'),
#' lty = 1)
#' }
interpolatePop <-
function(Pop1,
Pop2,
Date1,
Date2,
DesiredDate,
method = "linear",
roundoutput = FALSE) {
stopifnot(length(Pop1) == length(Pop2))
earlyDateDec <- dec.date(Date1)
laterDateDec <- dec.date(Date2)
desireDateDec <- dec.date(DesiredDate)
interpolateFactor <-
(desireDateDec - earlyDateDec) / (laterDateDec - earlyDateDec)
if (method == "exponential") {
adjustedPop <- Pop1 * exp(interpolateFactor * log(Pop2 / Pop1))
}
else {
adjustedPop <- Pop1 + (Pop2 - Pop1) * interpolateFactor
}
if (roundoutput) {
adjustedPop <- round(adjustedPop)
}
return(adjustedPop)
}
# TR: 2018-08-20 either complementing or replacing previous pop interpolater.
# this one more general.
###############################################################################
#' Interpolate between two population age distributions.
#' @description The interpolation is done by age using a linear, exponential, or power function. This comes from the PAS spreadsheet called \code{AGEINT}. Be aware that this is not cohort-component interpolation.
#' @param popmat numeric. An age-period matrix of data to interpolate over. Age in rows, time in columns.
#' @param datesIn vector of dates. The exact dates in each column. See details for ways to express it.
#' @param datesOut vector of dates. The desired dates to interpolate to. See details for ways to express it.
#' @param method string. The method to use for the interpolation, either \code{"linear"}, \code{"exponential"}, or \code{"power"}. Default \code{"linear"}.
#' @param power numeric power to interpolate by, if \code{method = "power"}. Default 2.
#' @param extrap logical. In case \code{datesOut} is out of range of `datesIn`, do extrapolation using slope in extreme pairwise. Default \code{FALSE}.
#' @param negatives logical. In case negative output are accepted, set to \code{TRUE}. Default \code{FALSE}.
#' @param ... arguments passed to \code{stats::approx}. For example, \code{rule}, which controls extrapolation behavior.
#' @details The age group structure of the output is the same as that of the input. Ideally, \code{datesOut} should be within the range of \code{datesIn}. If not, the left-side and right-side output are held constant outside the range if \code{rule = 2} is passed in, otherwise \code{NA} is returned (see examples). Dates can be given in three ways 1) a \code{Date} class object, 2) an unambiguous character string in the format \code{"YYYY-MM-DD"}, or 3) as a decimal date consisting in the year plus the fraction of the year passed as of the given date.
#'
#' For methods \code{"exponential"} and \code{"power"}, calculations are carried out using linear interpolation through \code{log(popmat)}, or \code{popmat^(1/power)} respectively, then back-transformed. If the data contain 0s, \code{method = "exponential"} will fail, but \code{"power"} would still generally work.
#'
#' @return numeric matrix (age-period) (or vector if \code{length(datesOut) == 1} of the interpolated data for the requested dates.
#' @author Tim Riffe
#' @references
#' \insertRef{PAS}{DemoTools}
#' @export
#' @importFrom stats approx
#'
#' @examples
#' # Example of interpolating over time series of age-structured
#' # data. NOTE: age classes must conform. They don't have to be
#' # uniform, just the same in each year.
#' popmat <- structure(c(2111460L, 1971927L, 1651421L, 1114149L, 921120L,
#' 859806L, 806857L, 847447L, 660666L, 567163L, 493799L, 322936L,
#' 320796L, 163876L, 133531L, 120866L, 2548265L, 2421813L, 2581979L,
#' 2141854L, 1522279L, 1321665L, 1036480L, 1024782L, 935787L, 789521L,
#' 719185L, 481997L, 479943L, 268777L, 202422L, 167962L, 3753848L,
#' 3270658L, 2930638L, 2692898L, 2222672L, 1788443L, 1514610L, 1491751L,
#' 1054937L, 972484L, 796138L, 673137L, 554010L, 352264L, 293308L,
#' 195307L, 3511776L, 3939121L, 4076601L, 3602857L, 2642620L, 2105063L,
#' 1993212L, 1914367L, 1616449L, 1408498L, 994936L, 776537L, 706189L,
#' 507085L, 315767L, 240302L, 3956664L, 3936424L, 3995805L, 4371774L,
#' 4012982L, 3144046L, 2406725L, 2301514L, 2061252L, 1871502L, 1538713L,
#' 1210822L, 896041L, 638954L, 401297L, 375648L), .Dim = c(16L,
#' 5L), .Dimnames = list(c("0", "5", "10", "15", "20", "25", "30",
#' "35", "40", "45", "50", "55", "60", "65", "70", "75"), c("1960",
#' "1976", "1986", "1996", "2006")))
#'
#' # We have irregularly spaced census inputs.
#' \dontrun{
#' matplot(seq(0,75,by=5),popmat,type='l',col=1:5)
#' text(20,popmat["20",],colnames(popmat),col=1:5)
#' }
#' # census dates as decimal: no need for year rounding.
#' # could also specify as "YYYY-MM-DD"
#' # (rounded to 2 digits here- not necessary)
#' datesIn <- c(1960.73, 1976.90, 1986.89, 1996.89, 2006.89)
#' # could ask for single years or anything
#' datesOut <- seq(1960, 2005, by = 5)
#'
#' # NOTE: rule is an argument to approx(), which does the work.
#' # if not passed in, 1960 is returned as NAs (outside the range
#' # of dates given). In this case, rule = 2 assumes constant
#' # equal to nearest neighbor, meaning 1960 data will be returned
#' # as equal to 1960.73 input data. So, function should only be
#' # allowed to extrapolate for short distances.
#' interp(popmat, datesIn, datesOut, "linear", rule = 2)
#' interp(popmat, datesIn, datesOut, "exponential", rule = 2)
#' interp(popmat, datesIn, datesOut, "power", rule = 2)
#'
#' # ----------------------------------------------------------
#' # standard example data, taken from AGEINT PAS spreadsheet.
#'
#' # YYYY-MM-DD dates as character
#' d1 <- "1980-04-07"
#' d2 <- "1990-10-06"
#' dout <- "1985-07-01"
#'
#' (p_lin <- interp(cbind(popA_earlier,popA_later),c(d1,d2),dout,method = "linear"))
#' (p_exp <- interp(cbind(popA_earlier,popA_later),c(d1,d2),dout,method = "exponential"))
#' (p_pow <- interp(cbind(popA_earlier,popA_later),c(d1,d2),dout,method = "power"))
#'
#' \dontshow{
#' print(dec.date(d1),digits=18)
#' print(dec.date(d2),digits=18)
#' print(dec.date(dout),digits=18)
#' # these were set to different dates: recreate above dates...
#' expcheck <- c(142611.789832724, 658653.225145641, 881642.532889494, 790424.835149364,
#' 631052.720366013, 523578.990638372, 426405.833143345, 352311.49478387,
#' 349576.729583329, 315026.304975883, 243331.949093535, 210952.668139616,
#' 179740.182668986, 149331.458251614, 112461.568530899, 75799.3288538202,
#' 43819.8095485345, 48869.8021063505)
#'
#' lincheck <- c(151269.209810283, 698637.560216026, 935163.70169507, 838408.524061807,
#' 669361.533645738, 555363.482079782, 452291.311354737, 373698.987198367,
#' 370798.204791894, 334150.355163834, 258103.707303228, 223758.803211712,
#' 190651.52632461, 158396.803770816, 119288.683114125, 80400.8181463556,
#' 46479.9437144632, 51836.5021355072)
#'
#' stopifnot(all(abs(expcheck - p_exp)<1e-6))
#' stopifnot(all(abs(lincheck - p_lin)<1e-6))
#' # do controlled spreadsheet test, passing in decimal dates, etc
#' }
#' \dontrun{
#' age <- c(0,1,seq(5,80,by=5))
#' plot(age, popA_later, type = 'l', lwd = 2, main = "Interpolation options")
#' lines(age, popA_earlier, lwd = 2, lty = "8282")
#' lines(age, p_lin,col = "blue")
#' lines(age, p_pow,col = gray(.4))
#' lines(age, p_exp,col = "red")
#' text(30, popA_earlier[8], d1, pos = 1, cex = 1)
#' text(20, popA_later[6], d2, pos = 4, cex = 1)
#' legend("topright", lty = 1, col = c("blue", gray(.4), "red"),
#' legend = c("linear", "power 2", "exponential"),
#' title = paste0("Interpolated 1985-07-01"))
#'
#' }
interp <- function(popmat,
datesIn,
datesOut,
method = c("linear", "exponential", "power"),
power = 2,
extrap = FALSE,
negatives = FALSE,
...) {
# ... args passed to stats::approx . Can give control over extrap assumptions
# IW: extrap=T for extrapolate following each slope in extreme pairwise.
# If not is explicit extrap=T, returns NA at those points
# a basic check
stopifnot(ncol(popmat) == length(datesIn))
# no sense documenting this wrapper ...
.approxwrap <- function(x, y, xout, extrap, ...) {
# interp
yout = stats::approx(x = x,
y = y,
xout = xout,
...)$y
if (extrap){
# extrap (each side)
rg <- range(x)
xext <- xout < rg[1]
if(any(xext))
yout[xext] <- (y[2]-y[1])/(x[2]-x[1])*(xout[xext]-x[1])+y[1]
xext <- xout > rg[2]
n <- length(y)
if(any(xext))
yout[xext] <- (y[n]-y[n-1])/(x[n]-x[n-1])*(xout[xext]-x[n-1])+y[n-1]
}
return(yout)
}
# -----------------------
# clean method declaration
# match.arg does partial matching and it's safer:
# match.arg("lin", c("linear", "exponential", "power"))
method <- tolower(match.arg(method,
choices = c("linear", "exponential", "power")))
# -----------------------
# coerce dates to decimal if necessary
datesIn <- dec.date(datesIn)
datesOut <- dec.date(datesOut)
# carry out transform 1
if (method == "exponential") {
if (any(popmat == 0)) {
stop(
"popmat contains 0s, so exponential interpolation won't work.\n
Either handle 0s then do this, or\n
maybe try method = 'power'."
)
}
popmat <- log(popmat)
}
if (method == "power") {
popmat <- popmat ^ (1 / power)
}
int <- apply(popmat,
1,
.approxwrap,
x = datesIn,
xout = datesOut,
extrap = extrap,
...)
dims <- dim(int)
if (!is.null(dims)) {
int <- t(int)
rownames(int) <- rownames(popmat)
colnames(int) <- datesOut
} else {
names(int) <- rownames(popmat)
}
# transform back
if (method == "exponential") {
int <- exp(int)
}
if (method == "power") {
int <- int ^ power
}
# IW: no negatives when extrapolate. Thinking in pop and lt expressions. Inactive when explicitly are accepted negatives
if(!negatives & all(!is.na(int)) & any(int < 0)){
cat("Negative values have been replaced with 0s.\nNegatives not accepted in population counts,\n fertility rates or life table functions.\nYou can allow negatives (e.g. interpolating net migration)\n by specifying negatives = TRUE")
int[int < 0] <- 0
}
int
}
timriffe/DemoTools documentation built on Oct. 14, 2024, 12:53 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions interp interpolatePop
Documented in interp interpolatePop
# Author: sean
# edited by TR 9-Dec-2017 & 28 Feb-2018
###############################################################################
#' Interpolate between two population age distributions.
#' @description The interpolation is done by age (not cohort) using a linear or exponential function. This comes from the PAS spreadsheet called ADJINT.
#' @param Pop1 numeric. A vector of demographic counts by age at time 1 (earlier date).
#' @param Pop2 numeric. A vector of demographic counts by age at time 2 (later date).
#' @param Date1 date. The date corresponding to the population age distribution at time 1. See details for ways to express it.
#' @param Date2 date. The date corresponding to the population age distribution at time 2. See details for ways to express it.
#' @param DesiredDate date. The desired date of the output population age distribution. See details for ways to express it.
#' @param method string. The method to use for the interpolation, either "linear" or "exponential". Default "linear".
#' @param roundoutput logical. Whether or not to return integers. Default \code{FALSE}.
#' @details The age group structure of the output is the same as that of the input. Ideally, \code{DesiredDate} should be between the Date1 and Date2.
#' Dates can be given in three ways 1) a \code{Date} class object, 2) an unambiguous character string in the format \code{"YYYY-MM-DD"},
#' or 3) as a decimal date consisting in the year plus the fraction of the year passed as of the given date.
#' @return A vector of the interpolated population for the requested date.
#' @author Sean Fennel
#' @references
#' \insertRef{PAS}{DemoTools}
#' @export
#'
#' @examples
#' # YYYY-MM-DD dates as character
#' EarlyDate <- "1980-04-07"
#' LaterDate <- "1990-10-06"
#' DesiredDate <- "1985-07-01"
#'
#' interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate)
#' interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate, method = "exponential")
#' \dontrun{
#'plot(popA_earlier, t ='l', col='red',
#' ylim = c(400,1220000),
#' ylab = 'The counts', xlab = 'Age groups')
#'
#'lines(interpolatePop(popA_earlier, popA_later, EarlyDate, LaterDate, DesiredDate),
#' t = 'l', col='black')
#'
#'lines(popA_later,
#' t = 'l', col='blue')
#'
#'legend(12,1000000,
#' legend = c('Pop in 1980-04-07',
#' 'Pop in 1985-07-01',
#' 'Pop in 1990-10-06'),
#' col=c('red','black','blue'),
#' lty = 1)
#' }
interpolatePop <-
function(Pop1,
Pop2,
Date1,
Date2,
DesiredDate,
method = "linear",
roundoutput = FALSE) {
stopifnot(length(Pop1) == length(Pop2))
earlyDateDec <- dec.date(Date1)
laterDateDec <- dec.date(Date2)
desireDateDec <- dec.date(DesiredDate)
interpolateFactor <-
(desireDateDec - earlyDateDec) / (laterDateDec - earlyDateDec)
if (method == "exponential") {
adjustedPop <- Pop1 * exp(interpolateFactor * log(Pop2 / Pop1))
}
else {
adjustedPop <- Pop1 + (Pop2 - Pop1) * interpolateFactor
}
if (roundoutput) {
adjustedPop <- round(adjustedPop)
}
return(adjustedPop)
}
# TR: 2018-08-20 either complementing or replacing previous pop interpolater.
# this one more general.
###############################################################################
#' Interpolate between two population age distributions.
#' @description The interpolation is done by age using a linear, exponential, or power function. This comes from the PAS spreadsheet called \code{AGEINT}. Be aware that this is not cohort-component interpolation.
#' @param popmat numeric. An age-period matrix of data to interpolate over. Age in rows, time in columns.
#' @param datesIn vector of dates. The exact dates in each column. See details for ways to express it.
#' @param datesOut vector of dates. The desired dates to interpolate to. See details for ways to express it.
#' @param method string. The method to use for the interpolation, either \code{"linear"}, \code{"exponential"}, or \code{"power"}. Default \code{"linear"}.
#' @param power numeric power to interpolate by, if \code{method = "power"}. Default 2.
#' @param extrap logical. In case \code{datesOut} is out of range of `datesIn`, do extrapolation using slope in extreme pairwise. Default \code{FALSE}.
#' @param negatives logical. In case negative output are accepted, set to \code{TRUE}. Default \code{FALSE}.
#' @param ... arguments passed to \code{stats::approx}. For example, \code{rule}, which controls extrapolation behavior.
#' @details The age group structure of the output is the same as that of the input. Ideally, \code{datesOut} should be within the range of \code{datesIn}. If not, the left-side and right-side output are held constant outside the range if \code{rule = 2} is passed in, otherwise \code{NA} is returned (see examples). Dates can be given in three ways 1) a \code{Date} class object, 2) an unambiguous character string in the format \code{"YYYY-MM-DD"}, or 3) as a decimal date consisting in the year plus the fraction of the year passed as of the given date.
#'
#' For methods \code{"exponential"} and \code{"power"}, calculations are carried out using linear interpolation through \code{log(popmat)}, or \code{popmat^(1/power)} respectively, then back-transformed. If the data contain 0s, \code{method = "exponential"} will fail, but \code{"power"} would still generally work.
#'
#' @return numeric matrix (age-period) (or vector if \code{length(datesOut) == 1} of the interpolated data for the requested dates.
#' @author Tim Riffe
#' @references
#' \insertRef{PAS}{DemoTools}
#' @export
#' @importFrom stats approx
#'
#' @examples
#' # Example of interpolating over time series of age-structured
#' # data. NOTE: age classes must conform. They don't have to be
#' # uniform, just the same in each year.
#' popmat <- structure(c(2111460L, 1971927L, 1651421L, 1114149L, 921120L,
#' 859806L, 806857L, 847447L, 660666L, 567163L, 493799L, 322936L,
#' 320796L, 163876L, 133531L, 120866L, 2548265L, 2421813L, 2581979L,
#' 2141854L, 1522279L, 1321665L, 1036480L, 1024782L, 935787L, 789521L,
#' 719185L, 481997L, 479943L, 268777L, 202422L, 167962L, 3753848L,
#' 3270658L, 2930638L, 2692898L, 2222672L, 1788443L, 1514610L, 1491751L,
#' 1054937L, 972484L, 796138L, 673137L, 554010L, 352264L, 293308L,
#' 195307L, 3511776L, 3939121L, 4076601L, 3602857L, 2642620L, 2105063L,
#' 1993212L, 1914367L, 1616449L, 1408498L, 994936L, 776537L, 706189L,
#' 507085L, 315767L, 240302L, 3956664L, 3936424L, 3995805L, 4371774L,
#' 4012982L, 3144046L, 2406725L, 2301514L, 2061252L, 1871502L, 1538713L,
#' 1210822L, 896041L, 638954L, 401297L, 375648L), .Dim = c(16L,
#' 5L), .Dimnames = list(c("0", "5", "10", "15", "20", "25", "30",
#' "35", "40", "45", "50", "55", "60", "65", "70", "75"), c("1960",
#' "1976", "1986", "1996", "2006")))
#'
#' # We have irregularly spaced census inputs.
#' \dontrun{
#' matplot(seq(0,75,by=5),popmat,type='l',col=1:5)
#' text(20,popmat["20",],colnames(popmat),col=1:5)
#' }
#' # census dates as decimal: no need for year rounding.
#' # could also specify as "YYYY-MM-DD"
#' # (rounded to 2 digits here- not necessary)
#' datesIn <- c(1960.73, 1976.90, 1986.89, 1996.89, 2006.89)
#' # could ask for single years or anything
#' datesOut <- seq(1960, 2005, by = 5)
#'
#' # NOTE: rule is an argument to approx(), which does the work.
#' # if not passed in, 1960 is returned as NAs (outside the range
#' # of dates given). In this case, rule = 2 assumes constant
#' # equal to nearest neighbor, meaning 1960 data will be returned
#' # as equal to 1960.73 input data. So, function should only be
#' # allowed to extrapolate for short distances.
#' interp(popmat, datesIn, datesOut, "linear", rule = 2)
#' interp(popmat, datesIn, datesOut, "exponential", rule = 2)
#' interp(popmat, datesIn, datesOut, "power", rule = 2)
#'
#' # ----------------------------------------------------------
#' # standard example data, taken from AGEINT PAS spreadsheet.
#'
#' # YYYY-MM-DD dates as character
#' d1 <- "1980-04-07"
#' d2 <- "1990-10-06"
#' dout <- "1985-07-01"
#'
#' (p_lin <- interp(cbind(popA_earlier,popA_later),c(d1,d2),dout,method = "linear"))
#' (p_exp <- interp(cbind(popA_earlier,popA_later),c(d1,d2),dout,method = "exponential"))
#' (p_pow <- interp(cbind(popA_earlier,popA_later),c(d1,d2),dout,method = "power"))
#'
#' \dontshow{
#' print(dec.date(d1),digits=18)
#' print(dec.date(d2),digits=18)
#' print(dec.date(dout),digits=18)
#' # these were set to different dates: recreate above dates...
#' expcheck <- c(142611.789832724, 658653.225145641, 881642.532889494, 790424.835149364,
#' 631052.720366013, 523578.990638372, 426405.833143345, 352311.49478387,
#' 349576.729583329, 315026.304975883, 243331.949093535, 210952.668139616,
#' 179740.182668986, 149331.458251614, 112461.568530899, 75799.3288538202,
#' 43819.8095485345, 48869.8021063505)
#'
#' lincheck <- c(151269.209810283, 698637.560216026, 935163.70169507, 838408.524061807,
#' 669361.533645738, 555363.482079782, 452291.311354737, 373698.987198367,
#' 370798.204791894, 334150.355163834, 258103.707303228, 223758.803211712,
#' 190651.52632461, 158396.803770816, 119288.683114125, 80400.8181463556,
#' 46479.9437144632, 51836.5021355072)
#'
#' stopifnot(all(abs(expcheck - p_exp)<1e-6))
#' stopifnot(all(abs(lincheck - p_lin)<1e-6))
#' # do controlled spreadsheet test, passing in decimal dates, etc
#' }
#' \dontrun{
#' age <- c(0,1,seq(5,80,by=5))
#' plot(age, popA_later, type = 'l', lwd = 2, main = "Interpolation options")
#' lines(age, popA_earlier, lwd = 2, lty = "8282")
#' lines(age, p_lin,col = "blue")
#' lines(age, p_pow,col = gray(.4))
#' lines(age, p_exp,col = "red")
#' text(30, popA_earlier[8], d1, pos = 1, cex = 1)
#' text(20, popA_later[6], d2, pos = 4, cex = 1)
#' legend("topright", lty = 1, col = c("blue", gray(.4), "red"),
#' legend = c("linear", "power 2", "exponential"),
#' title = paste0("Interpolated 1985-07-01"))
#'
#' }
interp <- function(popmat,
datesIn,
datesOut,
method = c("linear", "exponential", "power"),
power = 2,
extrap = FALSE,
negatives = FALSE,
...) {
# ... args passed to stats::approx . Can give control over extrap assumptions
# IW: extrap=T for extrapolate following each slope in extreme pairwise.
# If not is explicit extrap=T, returns NA at those points
# a basic check
stopifnot(ncol(popmat) == length(datesIn))
# no sense documenting this wrapper ...
.approxwrap <- function(x, y, xout, extrap, ...) {
# interp
yout = stats::approx(x = x,
y = y,
xout = xout,
...)$y
if (extrap){
# extrap (each side)
rg <- range(x)
xext <- xout < rg[1]
if(any(xext))
yout[xext] <- (y[2]-y[1])/(x[2]-x[1])*(xout[xext]-x[1])+y[1]
xext <- xout > rg[2]
n <- length(y)
if(any(xext))
yout[xext] <- (y[n]-y[n-1])/(x[n]-x[n-1])*(xout[xext]-x[n-1])+y[n-1]
}
return(yout)
}
# -----------------------
# clean method declaration
# match.arg does partial matching and it's safer:
# match.arg("lin", c("linear", "exponential", "power"))
method <- tolower(match.arg(method,
choices = c("linear", "exponential", "power")))
# -----------------------
# coerce dates to decimal if necessary
datesIn <- dec.date(datesIn)
datesOut <- dec.date(datesOut)
# carry out transform 1
if (method == "exponential") {
if (any(popmat == 0)) {
stop(
"popmat contains 0s, so exponential interpolation won't work.\n
Either handle 0s then do this, or\n
maybe try method = 'power'."
)
}
popmat <- log(popmat)
}
if (method == "power") {
popmat <- popmat ^ (1 / power)
}
int <- apply(popmat,
1,
.approxwrap,
x = datesIn,
xout = datesOut,
extrap = extrap,
...)
dims <- dim(int)
if (!is.null(dims)) {
int <- t(int)
rownames(int) <- rownames(popmat)
colnames(int) <- datesOut
} else {
names(int) <- rownames(popmat)
}
# transform back
if (method == "exponential") {
int <- exp(int)
}
if (method == "power") {
int <- int ^ power
}
# IW: no negatives when extrapolate. Thinking in pop and lt expressions. Inactive when explicitly are accepted negatives
if(!negatives & all(!is.na(int)) & any(int < 0)){
cat("Negative values have been replaced with 0s.\nNegatives not accepted in population counts,\n fertility rates or life table functions.\nYou can allow negatives (e.g. interpolating net migration)\n by specifying negatives = TRUE")
int[int < 0] <- 0
}
int
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.