info.rank {BCEA}  R Documentation 
Produces a plot similar to a Tornadoplot, 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.
info.rank(parameter, input, he, wtp = he$k[min(which(he$k >= he$ICER))], howManyPars = NULL, graph = c("base", "plotly"), ...)
parameter 
A vector of parameters for which the individual EVPPI should be calculated. This can be given as a string (or vector of strings) of names or a numeric vector, corresponding to the column numbers of important parameters. 
input 
A matrix containing the simulations for all the parameters monitored by the call to JAGS or BUGS. The matrix should have column names matching the names of the parameters and the values in the vector parameter should match at least one of those values. 
he 
A 
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 enging to use for plotting. Should (partial)match one of the two options "base" or "plotly". Default value is "base" 
... 
Additional options. These include graphical parameters that the user can specify:

res 
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, A. P. (2011). Probabilistic Sensitivity Analysis in Health Economics. Statistical Methods in Medical Research doi:10.1177/0962280211419832.
Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London