paramix (version 0.0.1)

distill_summary: Distillation Calculation Comparison Summary

Description

Implements several approaches to imputing higher resolution outcomes, then tables them up for convenient plotting.

Usage

distill_summary(alembic_dt, outcomes_dt, groupcol = names(outcomes_dt)[1])

Value

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

Arguments

alembic_dt

an alembic() return value

outcomes_dt

a long-format data.frame with a column either named from or model_from and a column value (other columns will be silently ignored)

groupcol

a string, the name of the outcome model group column. The outcomes_dt[[groupcol]] column must match the model_partition lower bounds, as provided when constructing the alembic_dt with alembic().

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 = 0:101, 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)