smaa (version 0.3-3)

smaa-package: 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.

Author

Gert van Valkenhoef

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.

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. [tools:::Rd_expr_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. [tools:::Rd_expr_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)