paramix (version 0.0.1)

parameter_summary: Parameter Calculation Comparison Summary

Description

Implements several approaches to computing partition-aggregated parameters, then tables them up for convenient plotting.

Usage

parameter_summary(f_param, f_pop, model_partition, resolution = 101L)

Value

a data.table, columns:

• model_category, a integer corresponding to which of the intervals of model_partition the x value is in

• x, a numeric series from the first to last elements of model_partition with length resolution

• method, a factor with levels:

  • f_val: f_param(x)

  • f_mid: f_param(x_mid), where x_mid is the midpoint x of the model_category

  • f_mean: f_param(weighted.mean(x, w)), where w defined by densities and model_category

  • mean_f: weighted.mean(f_param(x), w), same as previous

  • wm_f: the result as if having used paramix::blend(); this should be very similar to mean_f, though will be slightly different since blend uses integrate()

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

resolution

the number of points to calculate for the underlying f_param function. The default 101 points means 100 partitions.

Examples

# COVID IFR from Levin et al 2020 https://doi.org/10.1007/s10654-020-00698-1
f_param <- function(age_in_years) {
  (10^(-3.27 + 0.0524 * age_in_years))/100
}

densities <- data.frame(
  from = 0:101,
  weight = c(rep(1, 66), exp(-0.075 * 1:35), 0)
)

model_partition <- c(0, 5, 20, 65, 101)

ps_dt <- parameter_summary(f_param, densities, model_partition)
ps_dt

ggplot(ps_dt) + aes(x, y = value, color = method) +
  geom_line(data = \(dt) subset(dt, method == "f_val")) +
  geom_step(data = \(dt) subset(dt, method != "f_val")) +
  theme_bw() + theme(
    legend.position = "inside", legend.position.inside = c(0.05, 0.95),
    legend.justification = c(0, 1)
  ) + scale_color_discrete(
    "Method", labels = c(
      f_val = "f(x)", f_mid = "f(mid(x))", f_mean = "f(E[x])",
      mean_f = "discrete E[f(x)]", wm_f = "integrated E[f(x)]"
    )
  ) +
  scale_x_continuous("Age", breaks = seq(0, 100, by = 10)) +
  scale_y_log10("IFR", breaks = 10^c(-6, -4, -2, 0), limits = 10^c(-6, 0))