parameter_summary {paramix} | R Documentation |
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)
Arguments
f_param |
a function, |
f_pop |
like |
model_partition |
a numeric vector of cut points, which define the partitioning that will be used in the model; must be length > 1 |
resolution |
the number of points to calculate for the underlying
|
Value
a data.table
, columns:
-
model_category
, a integer corresponding to which of the intervals ofmodel_partition
thex
value is in -
x
, a numeric series from the first to last elements ofmodel_partition
with lengthresolution
-
method
, a factor with levels:-
f_val
:f_param(x)
-
f_mid
:f_param(x_mid)
, wherex_mid
is the midpoint x of themodel_category
-
f_mean
:f_param(weighted.mean(x, w))
, wherew
defined bydensities
andmodel_category
-
mean_f
:weighted.mean(f_param(x), w)
, same as previous -
wm_f
: the result as if having usedparamix::blend()
; this should be very similar tomean_f
, though will be slightly different sinceblend
usesintegrate()
-
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 = function(dt) subset(dt, method == "f_val")) +
geom_step(data = function(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))