dev/splitHomogenize.R
In timriffe/DemoTools: Standardize, Evaluate, and Adjust Demographic Data
# Author: tim
###############################################################################
# TODO: 1) splitMono() should be flexible wrt AgeInt, perhaps taking an AgeInt arg.
# follow same detection protocol as in splitUniform to produce intermediate pieces
# needed. Be sure to extend over final interval if OAG = FALSE.
# Counts -> Value
splitUniform <- function(Value, AgeInt, Age, OAG = TRUE, OAvalue = 1){
if (missing(Age) & missing(AgeInt)){
Age <- names2age(Value)
}
if (missing(AgeInt)){
# give 1 to final interval to preserve
AgeInt <- age2int(Age, OAG = OAG, OAvalue = OAvalue)
}
if (missing(Age)){
Age <- cumsum(AgeInt) - AgeInt
}
# discount for single
out <- rep(Value / AgeInt, times = AgeInt)
names(out) <- min(Age):(min(Age) + length(out) - 1)
out
}
# Prob: splitMono being used as Mono_smth() would have been intended.
# make splitting and smoothing separate so avoid confusion.
# we want splitMono() to emulate this behavior
# monoCloseout() depends on this, and splitOscillate() depends on monoCloseout().
# be sure to be able to get present dimensions of splitMono() outpout as option.
splitMono <- function(Value, AgeInt, Age, OAG = FALSE){
if (missing(Age) & missing(AgeInt)){
Age <- names2age(Value)
}
if (missing(AgeInt)){
# give 1 to final interval to preserve
AgeInt <- age2int(Age, OAG = OAG, OAvalue = 1)
}
if (missing(Age)){
Age <- int2age(AgeInt)
}
# if age is single return as-is
if (is_single(Age)){
return(Value)
}
# if last age Open, we preserve it
if (OAG){
N <- length(Value)
OAvalue <- Value[N]
Value <- Value[-N]
Age <- Age[-N]
AgeInt <- AgeInt[-N]
}
# if the final age is Open, then we should remove it and then
# stick it back on
AgePred <- c(min(Age), cumsum(AgeInt))
y <- c(0, cumsum(Value))
AgeS <- min(Age):sum(AgeInt)
y1 <- splinefun(y ~ AgePred, method = "monoH.FC")(AgeS)
out <- diff(y1)
names(out) <- AgeS[-length(AgeS)]
# The open age group is maintained as-is.
if (OAG){
out <- c(out, OAvalue)
names(out) <- AgeS
}
out
}
splitMono2(Value, OAG = FALSE) # OAG = TRUE implies identical behavior,
splitUniform(Value, OAG = FALSE) # OAG = FALSE splitUniform extends final age group through,
plot(0:104, splitUniform(Value, OAG = FALSE),type='s')
lines(0:104, splitMono2(Value, OAG = FALSE))
plot(0:100, splitUniform(Value, OAG = TRUE),type='s')
lines(0:100, splitMono2(Value, OAG = TRUE))
# if OAG = FALSE, identical shape
Value <- structure(c(88623, 90842, 93439, 96325, 99281, 102051, 104351,
106555, 109170, 112188, 113582, 112614, 108904, 102622, 95867,
80874, 60196, 37523, 17927, 5642, 1110), .Names = c("0", "5",
"10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60",
"65", "70", "75", "80", "85", "90", "95", "100"))
?rescaleAgeGroups
set.seed(3)
AgeIntRandom <- sample(1:5, size = 15,replace = TRUE)
AgeInt5 <- rep(5, 9)
original <- runif(45, min = 0, max = 100)
pop1 <- groupAges(original, 0:45, AgeN = int2ageN(AgeIntRandom, FALSE))
pop2 <- groupAges(original, 0:45, AgeN = int2ageN(AgeInt5, FALSE))
# inflate (in this case) pop2
perturb <- runif(length(pop2), min = 1.05, max = 1.2)
pop2 <- pop2 * perturb
# a recursively constrained solution
(sMonRF <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitMono,
recursive = FALSE))
(sMonRT <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitMono,
recursive = TRUE))
sMonRF - sMonRT
popmat <- structure(c(54170, 44775, 42142, 38464, 34406, 30386, 26933,
23481, 20602, 16489, 14248, 9928, 8490, 4801, 3599, 2048, 941,
326, 80, 17, 0, 57424, 44475, 41752, 39628, 34757, 30605, 27183,
23792, 20724, 17056, 14059, 10585, 8103, 5306, 3367, 2040, 963,
315, 80, 16, 1, 60272, 44780, 41804, 40229, 35155, 30978, 27456,
24097, 20873, 17546, 13990, 11146, 7841, 5738, 3184, 2062, 961,
311, 80, 15, 1, 62727, 45681, 42101, 40474, 35599, 31439, 27758,
24396, 21055, 17958, 14046, 11589, 7731, 6060, 3086, 2083, 949,
312, 79, 14, 1, 64816, 47137, 42508, 40532, 36083, 31940, 28092,
24693, 21274, 18299, 14223, 11906, 7785, 6255, 3090, 2084, 938,
316, 80, 14, 2), .Dim = c(21L, 5L), .Dimnames = list(c("0", "5",
"10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60",
"65", "70", "75", "80", "85", "90", "95", "100"), c("1950", "1951",
"1952", "1953", "1954")))
set.seed(3)
AgeIntRandom <- sample(1:5, size = 15,replace = TRUE)
AgeInt5 <- rep(5, 9)
original <- runif(45, min = 0, max = 100)
pop1 <- groupAges(original, 0:45, AgeN = int2ageN(AgeIntRandom, FALSE))
pop2 <- groupAges(original, 0:45, AgeN = int2ageN(AgeInt5, FALSE))
# inflate (in this case) pop2
perturb <- runif(length(pop2), min = 1.05, max = 1.2)
pop2 <- pop2 * perturb
# a recursively constrained solution
(pop1resc <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitUniform,
recursive = TRUE))
# a single pass adjustment (no recursion)
(pop1resc1 <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitUniform,
recursive = FALSE))
(pop1rescm <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitMono,
recursive = TRUE))
# a single pass adjustment (no recursion)
(pop1resc1m <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitMono,
recursive = FALSE))
## Not run:
# show before / after
plot(NULL,xlim=c(0,45),ylim=c(0,2),main = "Different (but integer) intervals",
xlab = "Age", ylab = "", axes = FALSE)
x1 <- c(0,cumsum(AgeIntRandom))
rect(x1[-length(x1)],1,x1[-1],2,col = gray(.8), border = "white")
x2 <- c(0,cumsum(AgeInt5))
rect(x2[-length(x2)],0,x2[-1],1,col = "palegreen1", border = "white")
text(23,1.5,"Original (arbitrary grouping)",font = 2, cex=1.5)
text(23,.5,"Standard to rescale to (arbitrary grouping)",font = 2, cex=1.5)
axis(1)
# adjustment factors:
plot(int2age(AgeInt5), perturb, ylim = c(0, 2))
points(int2age(AgeIntRandom), pop1resc / pop1, pch = 16)
# non-recursive is less disruptive for uniform
points(int2age(AgeIntRandom), pop1resc1 / pop1, pch = 16, col = "blue")
# show before / after under uniform (in pop1) assumption.
plot(NULL, xlim = c(0, 45), ylim = c(0, 150), main = "Uniform constraint")
lines(0:44, splitUniform(pop1, AgeInt = AgeIntRandom, OAG = FALSE), col = "red")
lines(0:44, splitUniform(pop2, AgeInt = AgeInt5, OAG = FALSE), col = "blue")
lines(0:44, splitUniform(pop1resc, AgeInt = AgeIntRandom, OAG = FALSE),
col = "orange", lty = 2, lwd = 2)
lines(0:44, splitUniform(pop1resc1, AgeInt = AgeIntRandom, OAG = FALSE),
col = "magenta", lty = 2, lwd = 2)
legend("topright",
lty = c(1, 1, 2, 2),
col = c("red", "blue", "orange", "magenta"),
lwd = c(1, 1, 2, 2),
legend = c("Original N1", "Prior N2",
"Rescaled N1 recursive", "Rescaled N1 1 pass"))
## End(Not run)
plot(NULL, xlim = c(0, 45), ylim = c(0, 150), main = "Uniform constraint")
lines(0:44, splitUniform(pop1, AgeInt = AgeIntRandom, OAG = FALSE), col = "red")
lines(0:44, splitUniform(pop2, AgeInt = AgeInt5, OAG = FALSE), col = "blue")
lines(0:44, splitUniform(pop1resc, AgeInt = AgeIntRandom, OAG = FALSE),
col = "orange", lty = 2, lwd = 2)
lines(0:44, splitUniform(pop1resc1, AgeInt = AgeIntRandom, OAG = FALSE),
col = "magenta", lty = 2, lwd = 2)
#legend("topright",
# lty = c(1, 1, 2, 2),
# col = c("red", "blue", "orange", "magenta"),
# lwd = c(1, 1, 2, 2),
# legend = c("Original N1", "Prior N2",
# "Rescaled N1 recursive", "Rescaled N1 1 pass"))
plot(NULL, xlim = c(0, 45), ylim = c(0, 150), main = "Uniform constraint")
lines(0:44, splitMono(pop1, AgeInt = AgeIntRandom, OAG = FALSE), col = "red")
lines(0:44, splitMono(pop2, AgeInt = AgeInt5, OAG = FALSE), col = "blue")
lines(0:44, splitMono(pop1rescm, AgeInt = AgeIntRandom, OAG = FALSE),
col = "orange", lty = 2, lwd = 2)
lines(0:44, splitMono(pop1resc1m, AgeInt = AgeIntRandom, OAG = FALSE),
col = "magenta", lty = 2, lwd = 2)
timriffe/DemoTools documentation built on Oct. 14, 2024, 12:53 p.m.
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# Author: tim
###############################################################################
# TODO: 1) splitMono() should be flexible wrt AgeInt, perhaps taking an AgeInt arg.
# follow same detection protocol as in splitUniform to produce intermediate pieces
# needed. Be sure to extend over final interval if OAG = FALSE.
# Counts -> Value
splitUniform <- function(Value, AgeInt, Age, OAG = TRUE, OAvalue = 1){
if (missing(Age) & missing(AgeInt)){
Age <- names2age(Value)
}
if (missing(AgeInt)){
# give 1 to final interval to preserve
AgeInt <- age2int(Age, OAG = OAG, OAvalue = OAvalue)
}
if (missing(Age)){
Age <- cumsum(AgeInt) - AgeInt
}
# discount for single
out <- rep(Value / AgeInt, times = AgeInt)
names(out) <- min(Age):(min(Age) + length(out) - 1)
out
}
# Prob: splitMono being used as Mono_smth() would have been intended.
# make splitting and smoothing separate so avoid confusion.
# we want splitMono() to emulate this behavior
# monoCloseout() depends on this, and splitOscillate() depends on monoCloseout().
# be sure to be able to get present dimensions of splitMono() outpout as option.
splitMono <- function(Value, AgeInt, Age, OAG = FALSE){
if (missing(Age) & missing(AgeInt)){
Age <- names2age(Value)
}
if (missing(AgeInt)){
# give 1 to final interval to preserve
AgeInt <- age2int(Age, OAG = OAG, OAvalue = 1)
}
if (missing(Age)){
Age <- int2age(AgeInt)
}
# if age is single return as-is
if (is_single(Age)){
return(Value)
}
# if last age Open, we preserve it
if (OAG){
N <- length(Value)
OAvalue <- Value[N]
Value <- Value[-N]
Age <- Age[-N]
AgeInt <- AgeInt[-N]
}
# if the final age is Open, then we should remove it and then
# stick it back on
AgePred <- c(min(Age), cumsum(AgeInt))
y <- c(0, cumsum(Value))
AgeS <- min(Age):sum(AgeInt)
y1 <- splinefun(y ~ AgePred, method = "monoH.FC")(AgeS)
out <- diff(y1)
names(out) <- AgeS[-length(AgeS)]
# The open age group is maintained as-is.
if (OAG){
out <- c(out, OAvalue)
names(out) <- AgeS
}
out
}
splitMono2(Value, OAG = FALSE) # OAG = TRUE implies identical behavior,
splitUniform(Value, OAG = FALSE) # OAG = FALSE splitUniform extends final age group through,
plot(0:104, splitUniform(Value, OAG = FALSE),type='s')
lines(0:104, splitMono2(Value, OAG = FALSE))
plot(0:100, splitUniform(Value, OAG = TRUE),type='s')
lines(0:100, splitMono2(Value, OAG = TRUE))
# if OAG = FALSE, identical shape
Value <- structure(c(88623, 90842, 93439, 96325, 99281, 102051, 104351,
106555, 109170, 112188, 113582, 112614, 108904, 102622, 95867,
80874, 60196, 37523, 17927, 5642, 1110), .Names = c("0", "5",
"10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60",
"65", "70", "75", "80", "85", "90", "95", "100"))
?rescaleAgeGroups
set.seed(3)
AgeIntRandom <- sample(1:5, size = 15,replace = TRUE)
AgeInt5 <- rep(5, 9)
original <- runif(45, min = 0, max = 100)
pop1 <- groupAges(original, 0:45, AgeN = int2ageN(AgeIntRandom, FALSE))
pop2 <- groupAges(original, 0:45, AgeN = int2ageN(AgeInt5, FALSE))
# inflate (in this case) pop2
perturb <- runif(length(pop2), min = 1.05, max = 1.2)
pop2 <- pop2 * perturb
# a recursively constrained solution
(sMonRF <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitMono,
recursive = FALSE))
(sMonRT <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitMono,
recursive = TRUE))
sMonRF - sMonRT
popmat <- structure(c(54170, 44775, 42142, 38464, 34406, 30386, 26933,
23481, 20602, 16489, 14248, 9928, 8490, 4801, 3599, 2048, 941,
326, 80, 17, 0, 57424, 44475, 41752, 39628, 34757, 30605, 27183,
23792, 20724, 17056, 14059, 10585, 8103, 5306, 3367, 2040, 963,
315, 80, 16, 1, 60272, 44780, 41804, 40229, 35155, 30978, 27456,
24097, 20873, 17546, 13990, 11146, 7841, 5738, 3184, 2062, 961,
311, 80, 15, 1, 62727, 45681, 42101, 40474, 35599, 31439, 27758,
24396, 21055, 17958, 14046, 11589, 7731, 6060, 3086, 2083, 949,
312, 79, 14, 1, 64816, 47137, 42508, 40532, 36083, 31940, 28092,
24693, 21274, 18299, 14223, 11906, 7785, 6255, 3090, 2084, 938,
316, 80, 14, 2), .Dim = c(21L, 5L), .Dimnames = list(c("0", "5",
"10", "15", "20", "25", "30", "35", "40", "45", "50", "55", "60",
"65", "70", "75", "80", "85", "90", "95", "100"), c("1950", "1951",
"1952", "1953", "1954")))
set.seed(3)
AgeIntRandom <- sample(1:5, size = 15,replace = TRUE)
AgeInt5 <- rep(5, 9)
original <- runif(45, min = 0, max = 100)
pop1 <- groupAges(original, 0:45, AgeN = int2ageN(AgeIntRandom, FALSE))
pop2 <- groupAges(original, 0:45, AgeN = int2ageN(AgeInt5, FALSE))
# inflate (in this case) pop2
perturb <- runif(length(pop2), min = 1.05, max = 1.2)
pop2 <- pop2 * perturb
# a recursively constrained solution
(pop1resc <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitUniform,
recursive = TRUE))
# a single pass adjustment (no recursion)
(pop1resc1 <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitUniform,
recursive = FALSE))
(pop1rescm <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitMono,
recursive = TRUE))
# a single pass adjustment (no recursion)
(pop1resc1m <- rescaleAgeGroups(Value1 = pop1,
AgeInt1 = AgeIntRandom,
Value2 = pop2,
AgeInt2 = AgeInt5,
splitfun = splitMono,
recursive = FALSE))
## Not run:
# show before / after
plot(NULL,xlim=c(0,45),ylim=c(0,2),main = "Different (but integer) intervals",
xlab = "Age", ylab = "", axes = FALSE)
x1 <- c(0,cumsum(AgeIntRandom))
rect(x1[-length(x1)],1,x1[-1],2,col = gray(.8), border = "white")
x2 <- c(0,cumsum(AgeInt5))
rect(x2[-length(x2)],0,x2[-1],1,col = "palegreen1", border = "white")
text(23,1.5,"Original (arbitrary grouping)",font = 2, cex=1.5)
text(23,.5,"Standard to rescale to (arbitrary grouping)",font = 2, cex=1.5)
axis(1)
# adjustment factors:
plot(int2age(AgeInt5), perturb, ylim = c(0, 2))
points(int2age(AgeIntRandom), pop1resc / pop1, pch = 16)
# non-recursive is less disruptive for uniform
points(int2age(AgeIntRandom), pop1resc1 / pop1, pch = 16, col = "blue")
# show before / after under uniform (in pop1) assumption.
plot(NULL, xlim = c(0, 45), ylim = c(0, 150), main = "Uniform constraint")
lines(0:44, splitUniform(pop1, AgeInt = AgeIntRandom, OAG = FALSE), col = "red")
lines(0:44, splitUniform(pop2, AgeInt = AgeInt5, OAG = FALSE), col = "blue")
lines(0:44, splitUniform(pop1resc, AgeInt = AgeIntRandom, OAG = FALSE),
col = "orange", lty = 2, lwd = 2)
lines(0:44, splitUniform(pop1resc1, AgeInt = AgeIntRandom, OAG = FALSE),
col = "magenta", lty = 2, lwd = 2)
legend("topright",
lty = c(1, 1, 2, 2),
col = c("red", "blue", "orange", "magenta"),
lwd = c(1, 1, 2, 2),
legend = c("Original N1", "Prior N2",
"Rescaled N1 recursive", "Rescaled N1 1 pass"))
## End(Not run)
plot(NULL, xlim = c(0, 45), ylim = c(0, 150), main = "Uniform constraint")
lines(0:44, splitUniform(pop1, AgeInt = AgeIntRandom, OAG = FALSE), col = "red")
lines(0:44, splitUniform(pop2, AgeInt = AgeInt5, OAG = FALSE), col = "blue")
lines(0:44, splitUniform(pop1resc, AgeInt = AgeIntRandom, OAG = FALSE),
col = "orange", lty = 2, lwd = 2)
lines(0:44, splitUniform(pop1resc1, AgeInt = AgeIntRandom, OAG = FALSE),
col = "magenta", lty = 2, lwd = 2)
#legend("topright",
# lty = c(1, 1, 2, 2),
# col = c("red", "blue", "orange", "magenta"),
# lwd = c(1, 1, 2, 2),
# legend = c("Original N1", "Prior N2",
# "Rescaled N1 recursive", "Rescaled N1 1 pass"))
plot(NULL, xlim = c(0, 45), ylim = c(0, 150), main = "Uniform constraint")
lines(0:44, splitMono(pop1, AgeInt = AgeIntRandom, OAG = FALSE), col = "red")
lines(0:44, splitMono(pop2, AgeInt = AgeInt5, OAG = FALSE), col = "blue")
lines(0:44, splitMono(pop1rescm, AgeInt = AgeIntRandom, OAG = FALSE),
col = "orange", lty = 2, lwd = 2)
lines(0:44, splitMono(pop1resc1m, AgeInt = AgeIntRandom, OAG = FALSE),
col = "magenta", lty = 2, lwd = 2)
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