distill_summary | R Documentation |
Implements several approaches to imputing higher resolution outcomes, then tables them up for convenient plotting.
distill_summary(alembic_dt, outcomes_dt, groupcol = names(outcomes_dt)[1])
alembic_dt |
an |
outcomes_dt |
a long-format |
groupcol |
a string, the name of the outcome model group column. The
|
a data.table
, columns:
partition
, the feature point corresponding to the value
value
, the translated outcomes_dt$value
method
, a factor with levels indicating how feature points are selected,
and how value
is weighted to those features:
f_mid
: features at the alembic_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
partition
f_mean
: the features at the model partition means
mean_f
: the features distributed according to the relative density in
the outcome partitions
wm_f
: the alembic()
approach
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 = 0:101, weight = c(rep(1, 66), exp(-0.075 * 1:35), 0)
)
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[from != max(from), .(value = sum(f_param(from) * weight)),
by = .(model_from = model_partition[findInterval(from, model_partition)])
]
ds_dt <- distill_summary(alembic_dt, model_outcomes_dt)
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