The full rank distribution behind cpaic_ranks(): the posterior probability
that each treatment (or component) takes each rank, in a named target
population. Ported from multinma::posterior_rank_probs() (Phillippo et
al. 2020) and extended, because under population adjustment the hierarchy is
a function of the target: the component effects are beta + Gamma x, so the
ranks move with x.
Usage
rank_probs(
object,
newdata = NULL,
what = c("treatment", "component"),
lower_is_better = FALSE,
cumulative = FALSE,
...
)Arguments
- object
A
cmlnmr()fit.- newdata
A one-row data frame giving the target population's effect-modifier values.
- what
"treatment"(default) or"component".- lower_is_better
If
TRUE, a smaller effect is preferred.- cumulative
Return cumulative rank probabilities (the quantity SUCRA summarizes) instead of the rankogram? Default
FALSE.- ...
Unused.
Value
A data frame of class cpaic_rank_probs with one row per (element,
rank) and columns element, rank_position, and probability.
Details
Elements that are not estimable at the target population are dropped from the
ranking set rather than ranked from the prior, exactly as in cpaic_ranks()
(Step 3 of Wigle et al. 2026); they are listed in the dropped attribute.
References
Wigle A, Beliveau A, Nikolakopoulou A, Lin L (2026). Creating Treatment and Component Hierarchies in Component Network Meta-Analysis.
Examples
if (FALSE) {
rp <- rank_probs(fit, newdata = data.frame(x1 = 0.5), what = "component")
plot(rp)
}