# Plots the probability that each intervention is the most cost-effective

Source:`R/mce.plot.R`

`mce.plot.Rd`

This function is deprecated. Use `ceac.plot()`

instead.
Plots the probability that each of the n_int interventions being analysed is
the most cost-effective.

## Arguments

- mce
The output of the call to the function

`multi.ce()`

.- pos
Parameter to set the position of the legend. Can be given in form of a string

`(bottom|top)(right|left)`

for base graphics and`bottom|top|left|right`

for ggplot2. It can be a two-elements vector, which specifies the relative position on the x and y axis respectively, or alternatively it can be in form of a logical variable, with`TRUE`

indicating to use the first standard and`FALSE`

to use the second one. Default value is`c(1,0.5)`

, that is on the right inside the plot area.- graph
A string used to select the graphical engine to use for plotting. Should (partial-)match the two options

`"base"`

or`"ggplot2"`

. Default value is`"base"`

.- ...
Optional arguments. For example, it is possible to specify the colours to be used in the plot. This is done in a vector

`color=c(...)`

. The length of the vector colors needs to be the same as the number of comparators included in the analysis, otherwise`BCEA`

will fall back to the default values (all black, or shades of grey)

## References

Baio G, Dawid aP (2011).
“Probabilistic sensitivity analysis in health economics.”
*Stat. Methods Med. Res.*, 1--20.
ISSN 1477-0334, doi:10.1177/0962280211419832
, https://pubmed.ncbi.nlm.nih.gov/21930515/.

Baio G (2013).
*Bayesian Methods in Health Economics*.
CRC.

## Examples

```
# See Baio G., Dawid A.P. (2011) for a detailed description of the
# Bayesian model and economic problem
if (FALSE) {
# 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
#
mce.plot(mce, # plots the probability of being most cost-effective
graph="base") # using base graphics
#
if(require(ggplot2)){
mce.plot(mce, # the same plot
graph="ggplot2") # using ggplot2 instead
}
}
```