Recomputes cpaic_ranks() over a grid of target populations, so that the
population dependence of the hierarchy is visible. Under population adjustment
a component's rank is a function of the target, and this is the object that
shows it.
Usage
rank_curve(
object,
em,
values,
at = NULL,
what = c("treatment", "component"),
lower_is_better = FALSE,
...
)Arguments
- object
A
cmlnmr()fit.- em
Name of the effect modifier to vary.
- values
Numeric vector of target values for
em.- at
Optional named vector fixing the other effect modifiers. Defaults to 0 for each.
- what, lower_is_better
See
cpaic_ranks().- ...
Unused.
Value
A data frame with one row per (element, target value), giving sucra,
mean_rank, p_best and estimate, plus estimable.
Examples
if (FALSE) {
rank_curve(fit, em = "x1", values = seq(-1, 1, by = 0.25), what = "component")
}