Distillation Calculation Comparison Summary
distill_summary.Rd
Implements several approaches to imputing higher resolution outcomes, then tables them up for convenient plotting.
Arguments
- model_outcomes_dt
a
data.table
(or convertable to such) with columnsmodel_from
andvalue
- alembic_dt
an
alembic()
return value
Value
a data.table
, columns:
partition
, the feature point corresponding to the valuevalue
, the translatedmodel_outcomes_dt$value
method
, a factor with levels indicating how feature points are selected, and howvalue
is weighted to those features:f_mid
: features at thealembic_dt
outcome partitions, each with value corresponding to the total value of the corresponding model partition, divided by the number of outcome partitions in that model partitionf_mean
: ...mean_f
: ...wm_f
: the
Examples
library(data.table)
f_param <- function(age_in_years) {
(10^(-3.27 + 0.0524 * age_in_years))/100
}
model_partition <- c(0, 5, 20, 65, 101)
density_dt <- data.table(
from = 0:100, weight = c(rep(1, 66), exp(-0.075 * 1:35))
)
alembic_dt <- alembic(
f_param, density_dt, model_partition, seq(0, 101, by = 1L)
)
# for simplicity, assume a uniform force-of-infection across ages =>
# infections proportion to population density.
model_outcomes_dt <- density_dt[, .(value = sum(f_param(from) * weight)),
by = .(model_from = model_partition[findInterval(from, model_partition)])
]
ds_dt <- distill_summary(model_outcomes_dt, alembic_dt)