R/lt_rule.R
In timriffe/DemoTools: Standardize, Evaluate, and Adjust Demographic Data
Defines functions
lt_rule_1a0
lt_rule_1a0_ak
lt_rule_ak_m0_a0
lt_rule_ak_q0_a0
lt_rule_4m0_m0
lt_rule_4m0_D0
Documented in
lt_rule_1a0
lt_rule_1a0_ak
lt_rule_4m0_D0
lt_rule_4m0_m0
lt_rule_ak_m0_a0
lt_rule_ak_q0_a0
# Author: tim
###############################################################################
# introducing a couple rules of thumb based on HMD relationships and simple assumptions.
# these are intended to kick in only if a demographic process-informed method isn't
# feasible.
#' rule of thumb for splitting infants from deaths under 5
#' @description Given the log crude death rate for ages 0-5, \eqn{log(M(0,5)} there is a rough 2-slope linear relationship with the log of the proportion of deaths under 5 that occur in the first year of life. This rule uses this two-slope linear relationship to estimate the proportion of deaths in age 0, and then split these from deaths under 5. The relationship is slightly different between males and females. This method is only to be invoked if neither population nor deaths are available in a separate tabulation for single age 0.
#' @details This is an elsewhere-undocumented relationship derived from the whole of the HMD. We used the \code{segmented} package to fit a 2-slope linear model. This can (and should) be reproduced using data from a more diverse collection, and even as-is the data should be subset to only those observations where deaths and populations were not split using HMD methods. You can reproduce the analysis given a data set in the format shown (but not executed) in the examples and following the code steps shown there.
#'
#' Regarding argument specification, \code{D04} is required, in which case either \code{M04} or \code{P04} can be given to continue.
#' @param D04 numeric. Deaths under age 5.
#' @param M04 numeric. Death rate under age 5.
#' @param P04 numeric. Exposure under age 5. A population estimate will do.
#' @param Sex character, either \code{"m"} or \code{"f"}.
#' @return Estimated deaths in age 0
#' @export
#' @references
#' \insertRef{Vito2008}{DemoTools}
#'
#' Human Mortality Database
#' @examples
#'
#' # to reproduce the coefficient estimation
#' # that the method is based on:
#' \dontrun{
#' # get data in this format:
#' # dput(head(Dat))
#' Dat <- structure(list(
#' lDR0 = c(-0.182459515740971, -0.147321312595521,
#' -0.138935222455608, -0.156873832361186,
#' -0.134782094278661, -0.135213007819874),
#' lM5 = c(-4.38578221044468, -4.56777854985643,
#' -4.58851248767896, -4.57684656734645,
#' -4.62854681692805, -4.61294106314254)),
#' .Names = c("lDR0", "lM5"),
#' class = c("data.frame"),
#' row.names = c(NA, -6L))
#' # where lDRO is log(D0 / D0_4)
#' # i.e. lof of proportion of deaths < 5 in first year of life
#' # and lM5 is log(M0_4)
#' # i.e. log of death rate in first 5 years of life
#'
#' # then first fit a linear model:
#' obj <- lm(lDR0~lM5, data = Dat)
#' # use segmented package:
#' seg <- segmented::segmented(obj)
#' # breakpoint:
#' seg$psi[2] # brk
#' # first intercept:
#' seg$coef[1] # int1
#' # first slope:
#' seg$coef[2] # s1
#' # difference in slope from 1st to second:
#' seg$coef[3] # ds1
#' # make Dat come from some other dataset and you'll get different coefs,
#' # it'd be possible to have these in families maybe, and in any case
#' # different for males and females. This is just a rough start, to be
#' # replaced if someone offers a superior method. These
#'
#' }
#' D0_4 <- 2e4
#' M0_4 <- 5/1000
#' P0_4 <- 4e6
#' # function usage straightforward, also vectorized.
#' D0 <- lt_rule_4m0_D0(D0_4, M0_4, Sex = "m")
#' # deaths in ages 1-4 are a separate step.
#' D1_4 <- D0_4 - D0
#'
#' # to get M0_4 it's best to follow these steps:
#' # 1) get M0 using lt_rule_4m0_m0(M0_4),
#' M0 <- lt_rule_4m0_m0(M0_4)
#' # 2) get denom using P0 = D0 / M0
#' P0 <- D0 / M0
#' # 3) get denom P1_4 as P0_4 - P0
#' P1_4 <- P0_4 - P0
#' # 4) M1_4 = D1_4 / P1_4.
#' M1_4 <- D1_4 / P1_4
#'
#' \dontrun{
#' plot(NULL, type = 'n', xlim = c(0, 5), ylim = c(1e-3, .025), log = "y",
#' xlab = "Age", ylab = "log(rate)")
#' segments(0, M0_4, 5, M0_4)
#' segments(0, M0, 1, M0)
#' segments(1, M1_4, 5, M1_4)
#' text(1, c(M0, M1_4, M0_4), c("M0", "M1_4", "M0_4"), pos = 3)
#' }
lt_rule_4m0_D0 <- function(D04, M04, P04, Sex = "m") {
stopifnot(Sex %in% c("m","f"))
if (missing(M04)) {
M5 <- D04 / P04
} else {
M5 <- M04
}
lM5 <- log(M5)
if (length(Sex) == 1 & length(lM5) > 1) {
Sex <- rep(Sex, length(lM5))
}
# get coefs, from segmented regression:
coefs <-
structure(
c(
-4.69468169447114,
-0.101269561161854,
0.0171297174685972,-0.194795399634278,
-4.38840457546843,
-0.0800885256382603,
0.0208247842765569,-0.192054558152916
),
.Dim = c(4L, 2L),
.Dimnames = list(c("brk",
"int1", "s1", "ds1"), c("f", "m"))
)
s2 <- coefs["s1", ] + coefs["ds1", ]
int2 <-
coefs["int1", ] + coefs["s1", ] * coefs["brk", ] - s2 * coefs["brk", ]
coefs <- rbind(coefs, s2, int2)
# based on a segmented regression of log(1D0/5D0)~log(5M0)
lrat <- ifelse(
Sex == "m",
# if male
ifelse(lM5 < coefs["brk", "m"],
coefs["int1", "m"] + lM5 * coefs["s1", "m"],
coefs["int2", "m"] + lM5 * coefs["s2", "m"]),
# if female
ifelse(lM5 < coefs["brk", "f"],
coefs["int1", "f"] + lM5 * coefs["s1", "f"],
coefs["int2", "f"] + lM5 * coefs["s2", "f"])
)
# back to ratio
rat <- exp(lrat)
prop0 <- 1 / (1 + rat)
# return D0
prop0 * D04
}
#' rule of thumb for estimating infant mortality rate from under 5 mortality
#' @description Given the log crude death rate for ages 0-5, \eqn{log(M(0,5)} there is a strong 2-slope linear relationship with the log of the infant death rate. With this method the user supplies the under 5 death rate and the infant death rate is returned. This method could be invoked if either population or (exclusive or) deaths are tabulated for infants, or if only the under 5 mortality rate is available
#' @details This is an elsewhere-undocumented relationship derived from the whole of the HMD. We used the \code{segmented} package to fit a 2-slope linear model. This can (and should) be reproduced using data from a more diverse collection, and even as-is the data should be subset to only those observations where deaths and populations were not split using HMD methods. You can reproduce the analysis given a data set in the format shown (but not executed) in the examples and following the code steps shown there.
#'
#' Regarding argument specification, either \code{M04} *or* \code{D04} and \code{P04} can both be given.
#' @param D04 numeric. Deaths under age 5.
#' @param M04 numeric. Death rate under age 5.
#' @param P04 numeric. Exposure under age 5. A population estimate will do.
#' @param Sex character, either \code{"m"} or \code{"f"}.
#' @return Estimated deaths in age 0
#' @export
#' @references
#' \insertRef{Vito2008}{DemoTools}
#'
#' Human Mortality Database
#' @examples
#'
#' # to reproduce the coefficient estimation
#' # that the method is based on:
#' \dontrun{
#' # get data in this format:
#' # dput(head(Dat))
#' Dat <- structure(
#' list(
#' lm0 = c(-2.92192434640927, -3.06165842367016,
#' -3.10778177736261, -3.14075804560425,
#' -3.20005407062761, -3.22489389719376
#' ),
#' lM5 = c(-4.38578221044468, -4.56777854985643,
#' -4.58851248767896, -4.57684656734645,
#' -4.62854681692805, -4.61294106314254)),
#' .Names = c("lm0", "lM5"),
#' class = c("data.frame"),
#' row.names = c(NA, -6L))
#' # where lm0 is log(M0)
#' # i.e. log of infant death rate
#' # and lM5 is log(M0_4)
#' # i.e. log of death rate in first 5 years of life
#'
#' # then first fit a linear model:
#' obj <- lm(lm0~lM5,data=Dat)
#' # use segmented package:
#' seg <- segmented::segmented(obj)
#' # breakpoint:
#' seg$psi[2] # brk
#' # first intercept:
#' seg$coef[1] # int1
#' # first slope:
#' seg$coef[2] # s1
#' # difference in slope from 1st to second:
#' seg$coef[3] # ds1
#' # make Dat come from some other dataset and you'll get different coefs,
#' # it'd be possible to have these in families maybe, and in any case
#' # different for males and females. This is just a rough start, to be
#' # replaced if someone offers a superior method. These
#'
#' }
#'
#' M0_4 <- 5/1000
#' M0 <- lt_rule_4m0_m0(M0_4)
#'
#' # M0 from this relationship is more reliable than other methods
#' # of independently splitting D0 or P0. So, if you're going to be
#' # splitting counts, then make use of M0 to force numerators and
#' # denominators to conform to this estimate.
#' D0_4 <- 2e4
#' P0_4 <- 4e6
#' # function usage straightforward, also vectorized.
#' D0 <- lt_rule_4m0_D0(D0_4, M0_4, Sex = "m")
#' # deaths in ages 1-4 are a separate step.
#' P0 <- D0 / M0
#' P1_4 <- P0_4 - P0
#' D1_4 <- D0_4 - D0
#' M1_4 <- D1_4 / P1_4
#' # and now we have all the pieces such that rate
#' # estimates conform.
#'
#' \dontrun{
#' plot(NULL, type = 'n', xlim = c(0, 5), ylim = c(1e-3, .025), log = "y",
#' xlab = "Age", ylab = "log(rate)")
#' segments(0, M0_4, 5, M0_4)
#' segments(0, M0, 1, M0)
#' segments(1, M1_4, 5, M1_4)
#' text(1, c(M0, M1_4, M0_4), c("M0", "M1_4", "M0_4"), pos = 3)
#' }
lt_rule_4m0_m0 <- function(M04, D04, P04, Sex ="m") {
stopifnot(Sex %in% c("m","f"))
if (missing(M04)) {
M5 <- D04 / P04
} else {
M5 <- M04
}
lM5 <- log(M5)
# handle lengths
if (length(Sex) == 1 & length(lM5) > 1) {
Sex <- rep(Sex, length(lM5))
}
# ------------------------------
# get coefs created
coefs <-
structure(
c(
-4.51388967799889,
1.49906532702989,
1.01391932870931,-0.226555050620318,
-4.76819124030249,
1.47765861943473,
1.01019112250369,-0.227872879237884
),
.Dim = c(4L, 2L),
.Dimnames = list(c("brk",
"int1", "s1", "ds1"), c("m", "f"))
)
s2 <- coefs["s1", ] + coefs["ds1", ]
int2 <-
coefs["int1", ] + coefs["s1", ] * coefs["brk", ] - s2 * coefs["brk", ]
coefs <- rbind(coefs, s2, int2)
# ------------------------------
# based on a segmented regression of log(1D0/5D0)~log(5M0)
lm0 <- ifelse(
Sex == "m",
# if male
ifelse(lM5 < coefs["brk", "m"],
coefs["int1", "m"] + lM5 * coefs["s1", "m"],
coefs["int2", "m"] + lM5 * coefs["s2", "m"]),
# if female
ifelse(lM5 < coefs["brk", "f"],
coefs["int1", "f"] + lM5 * coefs["s1", "f"],
coefs["int2", "f"] + lM5 * coefs["s2", "f"])
)
# back to rate
exp(lm0)
}
#' @title estimates a0 using the Andreev-Kingkade rule of thumb starting with IMR
#'
#' @description \code{AKq02a0} Andreev Kingkade a0 method. This version has a 3-part segmented linear model, based on cut points in q0. Code ported from HMDLifeTables.
#'
#' @param q0 a value or vector of values of q0, the death probability in the first year of life.
#' @param Sex either "m" or "f"
#'
#' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of a_0 values.
#'
#' @export
lt_rule_ak_q0_a0 <- function(q0, Sex ){
Sex <- rep(Sex, length(q0))
ifelse(Sex == "m",
ifelse(q0 < .0226, {0.1493 - 2.0367 * q0},
ifelse(q0 < 0.0785, {0.0244 + 3.4994 * q0},.2991)),
ifelse(q0 < 0.0170, {0.1490 - 2.0867 * q0},
ifelse(q0 < 0.0658, {0.0438 + 4.1075 * q0}, 0.3141))
)
}
#' @title estimates a0 using the Andreev-Kingkade rule of thumb starting with an event exposure rate
#'
#' @description These formulas and cutpoints are based on a supplementary analysis from Andreev & Kingkade. The original formulation was in terms of IMR. There is also an analytic path to convert M0 to q0 and then use the original q0 cutpoints. Code ported from HMD code base.
#'
#' @param M0 a value or vector of values of m0, the death risk in the first year of life.
#' @param Sex either "m" or "f"
#'
#' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
#'
#' @export
lt_rule_ak_m0_a0 <- function(M0, Sex ){
Sex <- rep(Sex, length(M0))
ifelse(Sex == "m",
ifelse(M0 < .0230, {0.14929 - 1.99545 * M0},
ifelse(M0 < 0.08307, {0.02832 + 3.26021 * M0},.29915)),
# f
ifelse(M0 < 0.01724, {0.14903 - 2.05527 * M0},
ifelse(M0 < 0.06891, {0.04667 + 3.88089 * M0}, 0.31411))
)
}
#' @title Andreev-Kingkade approximation for a0
#'
#' @description This function wraps the two approximations for a0 based on either q0 (IMR) or m0.
#'
#' @param M0 a value or vector of values of `1m0``, the death risk in the first year of life.
#' @param q0 a value or vector of values of `1q0``, the death probability in the first year of life, sometimes approximated with IMR.
#' @param Sex either `"m"` or `"f"`
#'
#' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
#'
#' @export
lt_rule_1a0_ak <- function(M0 = NULL, q0 = NULL, Sex){
stopifnot(sum(c(is.null(M0),is.null(q0))) == 1)
if (is.null(M0) & !is.null(q0)){
a0 <- lt_rule_ak_q0_a0(q0,Sex)
}
if (is.null(q0) & !is.null(M0)){
a0 <- lt_rule_ak_m0_a0(M0,Sex)
}
a0
}
#' @title calculate a0 in different ways
#'
#' @description This function wraps the Coale-Demeny and Andreev-Kingkade approximations for `a0`, which can come from `M0`, `q0`, or `IMR.`
#' @details If sex is given as both, \code{"b"}, then we calculate the male and female results separately, then weight them together using SRB. This is bad in theory, but the leverage is trivial, and it's better than using male or female coefs for the total population.
#'
#' @inheritParams lt_rule_1a0_cd
#' @param rule character. Either \code{"ak"} (Andreev-Kingkade) or \code{"cd"} (Coale-Demeny).
#' @param Sex character, either \code{"m"}, \code{"f"}, or \code{"b"}
#' @param q0 a value or vector of values of m0, the death risk in the first year of life.
#' @param SRB the sex ratio at birth (boys / girls), default 1.05
#' @details Neither Coale-Demeny nor Andreev-Kingkade have explicit a0 rules for both-sexes combined. There's not a good way to arrive at a both-sex a0 estimate without increasing data requirements (you'd need data from each sex, which are not always available). It's more convenient to blend sex-specific a0 estimates based on something. Here we use SRB to do this, for no other reason than it has an easy well-known default value. This is bad because it assumes no sex differences in infant mortality, but this choice has a trivial impact on results.
#' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
#'
#' @export
#'
lt_rule_1a0 <- function(rule = "ak",
M0 = NULL,
q0 = NULL,
Sex = 'm',
IMR = NA,
region = "w",
SRB = 1.05){
Sex <- match.arg(Sex, choices = c("m","f","b"))
region <- match.arg(region, choices = c("w","n","e","s"))
rule <- match.arg(rule, choices = c("ak","cd"))
# TR: experimental, if sex is b, we recurse?
if (Sex == "b"){
a0f <- lt_rule_1a0(rule=rule,M0=M0,q0=q0,Sex="f",IMR=IMR,region=region,SRB=SRB)
a0m <- lt_rule_1a0(rule=rule,M0=M0,q0=q0,Sex="m",IMR=IMR,region=region,SRB=SRB)
pm <- SRB / (1 + SRB)
a0 <- pm * a0m + (1 - pm) * (a0f)
return(a0)
} else {
# otherwise we have single-sex cases.
if (rule == "cd"){
a0 <- lt_rule_1a0_cd(M0 = M0,
IMR = IMR,
Sex = Sex,
region = region)
}
if (rule == "ak"){
if (is.null(M0) & is.null(q0) & !is.na(IMR)){
q0 <- IMR
}
a0 <- lt_rule_1a0_ak(M0 = M0,
q0 = q0,
Sex = Sex)
}
}
a0
}
timriffe/DemoTools documentation built on Oct. 14, 2024, 12:53 p.m.
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Defines functions lt_rule_1a0 lt_rule_1a0_ak lt_rule_ak_m0_a0 lt_rule_ak_q0_a0 lt_rule_4m0_m0 lt_rule_4m0_D0
Documented in lt_rule_1a0 lt_rule_1a0_ak lt_rule_4m0_D0 lt_rule_4m0_m0 lt_rule_ak_m0_a0 lt_rule_ak_q0_a0
# Author: tim
###############################################################################
# introducing a couple rules of thumb based on HMD relationships and simple assumptions.
# these are intended to kick in only if a demographic process-informed method isn't
# feasible.
#' rule of thumb for splitting infants from deaths under 5
#' @description Given the log crude death rate for ages 0-5, \eqn{log(M(0,5)} there is a rough 2-slope linear relationship with the log of the proportion of deaths under 5 that occur in the first year of life. This rule uses this two-slope linear relationship to estimate the proportion of deaths in age 0, and then split these from deaths under 5. The relationship is slightly different between males and females. This method is only to be invoked if neither population nor deaths are available in a separate tabulation for single age 0.
#' @details This is an elsewhere-undocumented relationship derived from the whole of the HMD. We used the \code{segmented} package to fit a 2-slope linear model. This can (and should) be reproduced using data from a more diverse collection, and even as-is the data should be subset to only those observations where deaths and populations were not split using HMD methods. You can reproduce the analysis given a data set in the format shown (but not executed) in the examples and following the code steps shown there.
#'
#' Regarding argument specification, \code{D04} is required, in which case either \code{M04} or \code{P04} can be given to continue.
#' @param D04 numeric. Deaths under age 5.
#' @param M04 numeric. Death rate under age 5.
#' @param P04 numeric. Exposure under age 5. A population estimate will do.
#' @param Sex character, either \code{"m"} or \code{"f"}.
#' @return Estimated deaths in age 0
#' @export
#' @references
#' \insertRef{Vito2008}{DemoTools}
#'
#' Human Mortality Database
#' @examples
#'
#' # to reproduce the coefficient estimation
#' # that the method is based on:
#' \dontrun{
#' # get data in this format:
#' # dput(head(Dat))
#' Dat <- structure(list(
#' lDR0 = c(-0.182459515740971, -0.147321312595521,
#' -0.138935222455608, -0.156873832361186,
#' -0.134782094278661, -0.135213007819874),
#' lM5 = c(-4.38578221044468, -4.56777854985643,
#' -4.58851248767896, -4.57684656734645,
#' -4.62854681692805, -4.61294106314254)),
#' .Names = c("lDR0", "lM5"),
#' class = c("data.frame"),
#' row.names = c(NA, -6L))
#' # where lDRO is log(D0 / D0_4)
#' # i.e. lof of proportion of deaths < 5 in first year of life
#' # and lM5 is log(M0_4)
#' # i.e. log of death rate in first 5 years of life
#'
#' # then first fit a linear model:
#' obj <- lm(lDR0~lM5, data = Dat)
#' # use segmented package:
#' seg <- segmented::segmented(obj)
#' # breakpoint:
#' seg$psi[2] # brk
#' # first intercept:
#' seg$coef[1] # int1
#' # first slope:
#' seg$coef[2] # s1
#' # difference in slope from 1st to second:
#' seg$coef[3] # ds1
#' # make Dat come from some other dataset and you'll get different coefs,
#' # it'd be possible to have these in families maybe, and in any case
#' # different for males and females. This is just a rough start, to be
#' # replaced if someone offers a superior method. These
#'
#' }
#' D0_4 <- 2e4
#' M0_4 <- 5/1000
#' P0_4 <- 4e6
#' # function usage straightforward, also vectorized.
#' D0 <- lt_rule_4m0_D0(D0_4, M0_4, Sex = "m")
#' # deaths in ages 1-4 are a separate step.
#' D1_4 <- D0_4 - D0
#'
#' # to get M0_4 it's best to follow these steps:
#' # 1) get M0 using lt_rule_4m0_m0(M0_4),
#' M0 <- lt_rule_4m0_m0(M0_4)
#' # 2) get denom using P0 = D0 / M0
#' P0 <- D0 / M0
#' # 3) get denom P1_4 as P0_4 - P0
#' P1_4 <- P0_4 - P0
#' # 4) M1_4 = D1_4 / P1_4.
#' M1_4 <- D1_4 / P1_4
#'
#' \dontrun{
#' plot(NULL, type = 'n', xlim = c(0, 5), ylim = c(1e-3, .025), log = "y",
#' xlab = "Age", ylab = "log(rate)")
#' segments(0, M0_4, 5, M0_4)
#' segments(0, M0, 1, M0)
#' segments(1, M1_4, 5, M1_4)
#' text(1, c(M0, M1_4, M0_4), c("M0", "M1_4", "M0_4"), pos = 3)
#' }
lt_rule_4m0_D0 <- function(D04, M04, P04, Sex = "m") {
stopifnot(Sex %in% c("m","f"))
if (missing(M04)) {
M5 <- D04 / P04
} else {
M5 <- M04
}
lM5 <- log(M5)
if (length(Sex) == 1 & length(lM5) > 1) {
Sex <- rep(Sex, length(lM5))
}
# get coefs, from segmented regression:
coefs <-
structure(
c(
-4.69468169447114,
-0.101269561161854,
0.0171297174685972,-0.194795399634278,
-4.38840457546843,
-0.0800885256382603,
0.0208247842765569,-0.192054558152916
),
.Dim = c(4L, 2L),
.Dimnames = list(c("brk",
"int1", "s1", "ds1"), c("f", "m"))
)
s2 <- coefs["s1", ] + coefs["ds1", ]
int2 <-
coefs["int1", ] + coefs["s1", ] * coefs["brk", ] - s2 * coefs["brk", ]
coefs <- rbind(coefs, s2, int2)
# based on a segmented regression of log(1D0/5D0)~log(5M0)
lrat <- ifelse(
Sex == "m",
# if male
ifelse(lM5 < coefs["brk", "m"],
coefs["int1", "m"] + lM5 * coefs["s1", "m"],
coefs["int2", "m"] + lM5 * coefs["s2", "m"]),
# if female
ifelse(lM5 < coefs["brk", "f"],
coefs["int1", "f"] + lM5 * coefs["s1", "f"],
coefs["int2", "f"] + lM5 * coefs["s2", "f"])
)
# back to ratio
rat <- exp(lrat)
prop0 <- 1 / (1 + rat)
# return D0
prop0 * D04
}
#' rule of thumb for estimating infant mortality rate from under 5 mortality
#' @description Given the log crude death rate for ages 0-5, \eqn{log(M(0,5)} there is a strong 2-slope linear relationship with the log of the infant death rate. With this method the user supplies the under 5 death rate and the infant death rate is returned. This method could be invoked if either population or (exclusive or) deaths are tabulated for infants, or if only the under 5 mortality rate is available
#' @details This is an elsewhere-undocumented relationship derived from the whole of the HMD. We used the \code{segmented} package to fit a 2-slope linear model. This can (and should) be reproduced using data from a more diverse collection, and even as-is the data should be subset to only those observations where deaths and populations were not split using HMD methods. You can reproduce the analysis given a data set in the format shown (but not executed) in the examples and following the code steps shown there.
#'
#' Regarding argument specification, either \code{M04} *or* \code{D04} and \code{P04} can both be given.
#' @param D04 numeric. Deaths under age 5.
#' @param M04 numeric. Death rate under age 5.
#' @param P04 numeric. Exposure under age 5. A population estimate will do.
#' @param Sex character, either \code{"m"} or \code{"f"}.
#' @return Estimated deaths in age 0
#' @export
#' @references
#' \insertRef{Vito2008}{DemoTools}
#'
#' Human Mortality Database
#' @examples
#'
#' # to reproduce the coefficient estimation
#' # that the method is based on:
#' \dontrun{
#' # get data in this format:
#' # dput(head(Dat))
#' Dat <- structure(
#' list(
#' lm0 = c(-2.92192434640927, -3.06165842367016,
#' -3.10778177736261, -3.14075804560425,
#' -3.20005407062761, -3.22489389719376
#' ),
#' lM5 = c(-4.38578221044468, -4.56777854985643,
#' -4.58851248767896, -4.57684656734645,
#' -4.62854681692805, -4.61294106314254)),
#' .Names = c("lm0", "lM5"),
#' class = c("data.frame"),
#' row.names = c(NA, -6L))
#' # where lm0 is log(M0)
#' # i.e. log of infant death rate
#' # and lM5 is log(M0_4)
#' # i.e. log of death rate in first 5 years of life
#'
#' # then first fit a linear model:
#' obj <- lm(lm0~lM5,data=Dat)
#' # use segmented package:
#' seg <- segmented::segmented(obj)
#' # breakpoint:
#' seg$psi[2] # brk
#' # first intercept:
#' seg$coef[1] # int1
#' # first slope:
#' seg$coef[2] # s1
#' # difference in slope from 1st to second:
#' seg$coef[3] # ds1
#' # make Dat come from some other dataset and you'll get different coefs,
#' # it'd be possible to have these in families maybe, and in any case
#' # different for males and females. This is just a rough start, to be
#' # replaced if someone offers a superior method. These
#'
#' }
#'
#' M0_4 <- 5/1000
#' M0 <- lt_rule_4m0_m0(M0_4)
#'
#' # M0 from this relationship is more reliable than other methods
#' # of independently splitting D0 or P0. So, if you're going to be
#' # splitting counts, then make use of M0 to force numerators and
#' # denominators to conform to this estimate.
#' D0_4 <- 2e4
#' P0_4 <- 4e6
#' # function usage straightforward, also vectorized.
#' D0 <- lt_rule_4m0_D0(D0_4, M0_4, Sex = "m")
#' # deaths in ages 1-4 are a separate step.
#' P0 <- D0 / M0
#' P1_4 <- P0_4 - P0
#' D1_4 <- D0_4 - D0
#' M1_4 <- D1_4 / P1_4
#' # and now we have all the pieces such that rate
#' # estimates conform.
#'
#' \dontrun{
#' plot(NULL, type = 'n', xlim = c(0, 5), ylim = c(1e-3, .025), log = "y",
#' xlab = "Age", ylab = "log(rate)")
#' segments(0, M0_4, 5, M0_4)
#' segments(0, M0, 1, M0)
#' segments(1, M1_4, 5, M1_4)
#' text(1, c(M0, M1_4, M0_4), c("M0", "M1_4", "M0_4"), pos = 3)
#' }
lt_rule_4m0_m0 <- function(M04, D04, P04, Sex ="m") {
stopifnot(Sex %in% c("m","f"))
if (missing(M04)) {
M5 <- D04 / P04
} else {
M5 <- M04
}
lM5 <- log(M5)
# handle lengths
if (length(Sex) == 1 & length(lM5) > 1) {
Sex <- rep(Sex, length(lM5))
}
# ------------------------------
# get coefs created
coefs <-
structure(
c(
-4.51388967799889,
1.49906532702989,
1.01391932870931,-0.226555050620318,
-4.76819124030249,
1.47765861943473,
1.01019112250369,-0.227872879237884
),
.Dim = c(4L, 2L),
.Dimnames = list(c("brk",
"int1", "s1", "ds1"), c("m", "f"))
)
s2 <- coefs["s1", ] + coefs["ds1", ]
int2 <-
coefs["int1", ] + coefs["s1", ] * coefs["brk", ] - s2 * coefs["brk", ]
coefs <- rbind(coefs, s2, int2)
# ------------------------------
# based on a segmented regression of log(1D0/5D0)~log(5M0)
lm0 <- ifelse(
Sex == "m",
# if male
ifelse(lM5 < coefs["brk", "m"],
coefs["int1", "m"] + lM5 * coefs["s1", "m"],
coefs["int2", "m"] + lM5 * coefs["s2", "m"]),
# if female
ifelse(lM5 < coefs["brk", "f"],
coefs["int1", "f"] + lM5 * coefs["s1", "f"],
coefs["int2", "f"] + lM5 * coefs["s2", "f"])
)
# back to rate
exp(lm0)
}
#' @title estimates a0 using the Andreev-Kingkade rule of thumb starting with IMR
#'
#' @description \code{AKq02a0} Andreev Kingkade a0 method. This version has a 3-part segmented linear model, based on cut points in q0. Code ported from HMDLifeTables.
#'
#' @param q0 a value or vector of values of q0, the death probability in the first year of life.
#' @param Sex either "m" or "f"
#'
#' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of a_0 values.
#'
#' @export
lt_rule_ak_q0_a0 <- function(q0, Sex ){
Sex <- rep(Sex, length(q0))
ifelse(Sex == "m",
ifelse(q0 < .0226, {0.1493 - 2.0367 * q0},
ifelse(q0 < 0.0785, {0.0244 + 3.4994 * q0},.2991)),
ifelse(q0 < 0.0170, {0.1490 - 2.0867 * q0},
ifelse(q0 < 0.0658, {0.0438 + 4.1075 * q0}, 0.3141))
)
}
#' @title estimates a0 using the Andreev-Kingkade rule of thumb starting with an event exposure rate
#'
#' @description These formulas and cutpoints are based on a supplementary analysis from Andreev & Kingkade. The original formulation was in terms of IMR. There is also an analytic path to convert M0 to q0 and then use the original q0 cutpoints. Code ported from HMD code base.
#'
#' @param M0 a value or vector of values of m0, the death risk in the first year of life.
#' @param Sex either "m" or "f"
#'
#' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
#'
#' @export
lt_rule_ak_m0_a0 <- function(M0, Sex ){
Sex <- rep(Sex, length(M0))
ifelse(Sex == "m",
ifelse(M0 < .0230, {0.14929 - 1.99545 * M0},
ifelse(M0 < 0.08307, {0.02832 + 3.26021 * M0},.29915)),
# f
ifelse(M0 < 0.01724, {0.14903 - 2.05527 * M0},
ifelse(M0 < 0.06891, {0.04667 + 3.88089 * M0}, 0.31411))
)
}
#' @title Andreev-Kingkade approximation for a0
#'
#' @description This function wraps the two approximations for a0 based on either q0 (IMR) or m0.
#'
#' @param M0 a value or vector of values of `1m0``, the death risk in the first year of life.
#' @param q0 a value or vector of values of `1q0``, the death probability in the first year of life, sometimes approximated with IMR.
#' @param Sex either `"m"` or `"f"`
#'
#' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
#'
#' @export
lt_rule_1a0_ak <- function(M0 = NULL, q0 = NULL, Sex){
stopifnot(sum(c(is.null(M0),is.null(q0))) == 1)
if (is.null(M0) & !is.null(q0)){
a0 <- lt_rule_ak_q0_a0(q0,Sex)
}
if (is.null(q0) & !is.null(M0)){
a0 <- lt_rule_ak_m0_a0(M0,Sex)
}
a0
}
#' @title calculate a0 in different ways
#'
#' @description This function wraps the Coale-Demeny and Andreev-Kingkade approximations for `a0`, which can come from `M0`, `q0`, or `IMR.`
#' @details If sex is given as both, \code{"b"}, then we calculate the male and female results separately, then weight them together using SRB. This is bad in theory, but the leverage is trivial, and it's better than using male or female coefs for the total population.
#'
#' @inheritParams lt_rule_1a0_cd
#' @param rule character. Either \code{"ak"} (Andreev-Kingkade) or \code{"cd"} (Coale-Demeny).
#' @param Sex character, either \code{"m"}, \code{"f"}, or \code{"b"}
#' @param q0 a value or vector of values of m0, the death risk in the first year of life.
#' @param SRB the sex ratio at birth (boys / girls), default 1.05
#' @details Neither Coale-Demeny nor Andreev-Kingkade have explicit a0 rules for both-sexes combined. There's not a good way to arrive at a both-sex a0 estimate without increasing data requirements (you'd need data from each sex, which are not always available). It's more convenient to blend sex-specific a0 estimates based on something. Here we use SRB to do this, for no other reason than it has an easy well-known default value. This is bad because it assumes no sex differences in infant mortality, but this choice has a trivial impact on results.
#' @return a0, the estimated average age at death of those dying in the first year of life, either a single value or a vector of values.
#'
#' @export
#'
lt_rule_1a0 <- function(rule = "ak",
M0 = NULL,
q0 = NULL,
Sex = 'm',
IMR = NA,
region = "w",
SRB = 1.05){
Sex <- match.arg(Sex, choices = c("m","f","b"))
region <- match.arg(region, choices = c("w","n","e","s"))
rule <- match.arg(rule, choices = c("ak","cd"))
# TR: experimental, if sex is b, we recurse?
if (Sex == "b"){
a0f <- lt_rule_1a0(rule=rule,M0=M0,q0=q0,Sex="f",IMR=IMR,region=region,SRB=SRB)
a0m <- lt_rule_1a0(rule=rule,M0=M0,q0=q0,Sex="m",IMR=IMR,region=region,SRB=SRB)
pm <- SRB / (1 + SRB)
a0 <- pm * a0m + (1 - pm) * (a0f)
return(a0)
} else {
# otherwise we have single-sex cases.
if (rule == "cd"){
a0 <- lt_rule_1a0_cd(M0 = M0,
IMR = IMR,
Sex = Sex,
region = region)
}
if (rule == "ak"){
if (is.null(M0) & is.null(q0) & !is.na(IMR)){
q0 <- IMR
}
a0 <- lt_rule_1a0_ak(M0 = M0,
q0 = q0,
Sex = Sex)
}
}
a0
}
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