splitOscillate: An oscillatory average of age splits.

In timriffe/DemoTools: Standardize, Evaluate, and Adjust Demographic Data

An oscillatory average of age splits.

Description

Single ages can be grouped into 5-year age groups in 5 ways by staggering terminal digits. This method is a bit smoother than the standard Sprague or Beers methods, but not as smooth as grabill().

Usage

splitOscillate(
  Value,
  Age = 1:length(Value) - 1,
  OAG = TRUE,
  splitfun = graduate_sprague,
  closeout = "mono",
  pivotAge = 90,
  ...
)

Arguments

Value	numeric. Vector of single age counts.
Age	integer. Vector of single ages.
OAG	logical. Whether or not the last value is the open age group. Default TRUE.
splitfun	function used to split at each digit grouping. Default sprague().
closeout	logical or character. Default "mono".
pivotAge	integer. Age to start blending in closeout values.
...	optional arguments passed to splitfun().

Details

This function works on a single vector of single-age counts, not on a matrix. Results are not constrained to any particular age group, but are constrained to the total count. Negatives, NA, or NaN values are ignored in averaging. This can happen in older ages . It is recommended to run monoCloseout() or similar after the oscillatory split in such situations.

Value

Numeric vector of smoothed counts.

References

\insertRef

booth2015demographicDemoTools

Examples

# code currently breaking, needs to be revisited and updates completed, sorry
## Not run: 

Value     <- structure(pop1m_ind, .Names = 0:100)
#barplot(Value, main = "yup, these have heaping!")
# this is the basic case we compare with:
pop0      <- graduate_sprague(groupAges(Value), Age = 0:100,  OAG = TRUE)
# note: this function needs single ages to work because
# ages are grouped into 5-year age groups in 5 different ways.
# breaks
#pop1     <- splitOscillate(Value, OAG = TRUE, splitfun = graduate_sprague)
pop2      <- splitOscillate(Value, OAG = TRUE, splitfun = beers)
# what's smoother, splitOscillate() or graduate_grabill()?
# note, same closeout problem, can be handled by graduate_mono_closeout()
pop3      <- graduate_grabill(Value, OAG = TRUE)
# and technically you could give grabill as splitfun too
pop4      <- splitOscillate(Value, OAG = TRUE, splitfun = graduate_grabill)

Age       <- 0:100
plot(Age, Value)
lines(Age, pop0, col = "blue")
# slightly smoother (also shifted though)
lines(Age, pop1)
# only different at very high ages, small nrs
lines(Age, pop2, col = "red", lty = 2, lwd = 2)
lines(Age, pop3, col = "magenta")
lines(Age, pop4, col = "orange", lty = 2)
legend("topright",
lty = c(1,1,2,1,2),
lwd = c(1,1,2,1,1),
col = c("blue","black","red","magenta","orange"),
		legend = c("graduate_sprague()",
                "splitOscillate(splitfun = graduate_sprague)",
				   "splitOscillate(splitfun = beers)",
				   "graduate_grabill()",
                "splitOscillate(splitfun = graduate_grabill)"))

# index of dissimilarity
ID(Value, pop0) # original vs sprague
ID(pop0,pop1) # sprague vs sprague osc
ID(pop1,pop2) # sprague osc vs beers osc
ID(pop2,pop3) # beers osc vs grabill
ID(pop3,pop4) # grabill vs grabill osc
# measure of smoothness:
mean(abs(diff(Value)))
mean(abs(diff(pop0)))
mean(abs(diff(pop1)))
mean(abs(diff(pop2)))
mean(abs(diff(pop3)))
mean(abs(diff(pop4)))

## End(Not run)

timriffe/DemoTools documentation built on Oct. 14, 2024, 12:53 p.m.