smaa-package {smaa} | R Documentation |
SMAA: Stochastic Multi-criteria Acceptability Analysis
Description
This R package implements the Stochastic Multi-criteria Acceptability Analysis (SMAA) family of methods for stochastic Multiple Criteria Decision Analysis (MCDA). In SMAA methods, uncertainty in criteria measurements and incomplete information on the weights are accounted for by Monte Carlo integration of probability distributions.
Details
The smaa
function implements the core Monte Carlo integration method. It calculates
the SMAA decision metrics (rank acceptabilities and central weights) in one go.
Alternatively, the smaa.values
, smaa.ranks
, smaa.ra
, and
smaa.cw
perform the individual steps. Note that smaa
is slightly more
efficient because it does not store the alternatives' values or rankings.
The hitandrun-package is complementary to this package in that it provides methods for sampling weights when incomplete preference information is available in the form of linear constraints on the weight vector.
Author(s)
Gert van Valkenhoef
References
T. Tervonen and J.R. Figueira (2008), A survey on stochastic multicriteria acceptability analysis methods, Journal of Multi-Criteria Decision Analysis 15(1-2):1-14. [doi: 10.1002/mcda.407]
T. Tervonen, G. van Valkenhoef, N. Basturk, and D. Postmus (2012), Hit-And-Run enables efficient weight generation for simulation-based multiple criteria decision analysis, European Journal of Operational Research 224(3):552-559. [doi: 10.1016/j.ejor.2012.08.026]
Examples
N <- 1E4; m <- 2; n <- 3
meas <- dget(system.file("extdata/thrombo-meas.txt.gz", package="smaa"))
pref <- dget(system.file("extdata/thrombo-weights-nopref.txt.gz", package="smaa"))
# Calculate SMAA metrics (one-stage)
result <- smaa(meas, pref)
print(result)
# Calculate SMAA metrics (multi-stage)
values <- smaa.values(meas, pref)
summary(values)
ranks <- smaa.ranks(values)
smaa.ra(ranks)
smaa.entropy.ranking(ranks)
smaa.cw(ranks, pref)
# Calculate confidence factors
smaa.cf(meas, result$cw)