info.rank.bcea {BCEA}  R Documentation 
Produces a plot similar to a tornado plot, but based on the analysis of the EVPPI. For each parameter and value of the willingnesstopay threshold, a barchart is plotted to describe the ratio of EVPPI (specific to that parameter) to EVPI. This represents the relative ‘importance’ of each parameter in terms of the expected value of information.
## S3 method for class 'bcea'
info.rank(
he,
inp,
wtp = NULL,
howManyPars = NA,
graph = c("base", "ggplot2", "plotly"),
rel = TRUE,
...
)
info.rank(he, ...)
he 
A 
inp 
Named list from running

wtp 
A value of the wtp for which the analysis should be performed. If not specified then the breakeven point for the current model will be used. 
howManyPars 
Optional maximum number of parameters to be included in the bar plot. Includes all parameters by default. 
graph 
A string used to select the graphical engine to use for plotting. Should (partial)match one of the two options "base" or "plotly". Default value is "base" 
rel 
Logical argument that specifies whether the ratio of
EVPPI to EVPI ( 
... 
Additional options. These include graphical parameters that the user can specify:

With base graphics: A data.frame containing the ranking of the parameters with the value of the selected summary, for the chosen wtp; with plotly: a plotly object, incorporating in the $rank element the data.frame as above. The function produces a 'Inforank' plot. This is an extension of standard 'Tornado plots' and presents a ranking of the model parameters in terms of their impact on the expected value of information. For each parameter, the specific individual EVPPI is computed and used to measure the impact of uncertainty in that parameter over the decisionmaking process, in terms of how large the expected value of gaining more information is.
Anna Heath, Gianluca Baio, Andrea Berardi
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 14770334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
## Not run:
# Load the postprocessed results of the MCMC simulation model
# original JAGS output is can be downloaded from here
# https://gianluca.statistica.it/book/bcea/code/vaccine.RData
data("Vaccine")
m < bcea(eff, cost)
inp < createInputs(vaccine_mat)
info.rank(m, inp)
info.rank(m, inp, graph = "base")
info.rank(m, inp, graph = "plotly")
info.rank(m, inp, graph = "ggplot2")
## End(Not run)