Computes and plots the probability that each of the `n_int`

interventions
being analysed is the most cost-effective and the cost-effectiveness
acceptability frontier.

## Arguments

- he
A

`bcea`

object containing the results of the Bayesian modelling and the economic evaluation.

## Value

Original `bcea`

object (list) of class "pairwise" with additional:

- p_best_interv
A matrix including the probability that each intervention is the most cost-effective for all values of the willingness to pay parameter

- ceaf
A vector containing the cost-effectiveness acceptability frontier

## Examples

```
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
# Load the processed results of the MCMC simulation model
data(Vaccine)
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, c=cost, # defines the variables of
# effectiveness and cost
ref=2, # selects the 2nd row of (e,c)
# as containing the reference intervention
interventions=treats, # defines the labels to be associated
# with each intervention
Kmax=50000, # maximum value possible for the willingness
# to pay threshold; implies that k is chosen
# in a grid from the interval (0,Kmax)
plot=FALSE # inhibits graphical output
)
mce <- multi.ce(m) # uses the results of the economic analysis
ceac.plot(mce)
ceaf.plot(mce)
```