Create the Blending and Distilling Object

Usage

alembic(
  f_param,
  f_pop,
  model_partition,
  output_partition,
  pars_interp_opts = interpolate_opts(fun = stats::splinefun, kind = "point", method =
    "natural"),
  pop_interp_opts = interpolate_opts(fun = stats::approxfun, kind = "integral", method =
    "constant", yleft = 0, yright = 0)
)

Arguments

f_param: a function, f(x) which transforms the feature (e.g. age), to yield the parameter values. Alternatively, a data.frame where the first column is the feature and the second is the parameter; see xy.coords() for details. If the latter, combined with pars_interp_opts, and defaulting to spline interpolation.
f_pop: like f_param, either a density function (though it does not have to integrate to 1 like a pdf) or a data.frame of values. If the latter, it is treated as a series of populations within intervals, and then interpolated with pop_interp_opts to create a density function.
model_partition: a numeric vector of cut points, which define the partitioning that will be used in the model
output_partition: the partition of the underlying feature
pars_interp_opts: a list, minimally with an element fun, corresponding to an interpolation function. Defaults to splinefun() "natural" interpolation.
pop_interp_opts: ibid, but for density. Defaults to approxfun() "constant" interpolation.

Value

a data.table with columns model_partition, output_partition, weight and relpop. The first two columns identify partition lower bounds, for both the model and output, the other values are associated with; the combination of model_partition and output_partition forms a unique identifier, but individually they may appear multiple times. Generally, this object is only useful as an input to the blend() and distill() tools.

Examples

ifr_levin <- function(age_in_years) {
  (10^(-3.27 + 0.0524 * age_in_years))/100
}
age_limits <- c(seq(0, 69, by = 5), 70, 80, 101)
age_pyramid <- data.frame(
  from = 0:101, weight = ifelse(0:101 < 65, 1, .99^(0:101-64))
)
age_pyramid$weight[102] <- 0
# flat age distribution, then 1% annual deaths, no one lives past 101
ifr_alembic <- alembic(ifr_levin, age_pyramid, age_limits, 0:101)