Skip to contents

Test how far a decision ranking moves when the weights are perturbed

Source: R/decision_sensitivity.R
sensitivity_analysis.Rd

Perturbs each criterion weight up and down by delta, renormalises the weight vector to sum to 1, reruns the same ranking method, and compares the perturbed ranking against the base ranking. A ranking that survives this unchanged is one a reviewer can be told is robust to the weights. A ranking whose leader changes under a 5 percent nudge is not.

Usage

sensitivity_analysis(
  x,
  weights,
  criteria_types,
  method = "topsis",
  delta = 0.05,
  alternatives = NULL,
  criteria = NULL,
  ...
)

Arguments

x

Numeric performance matrix, alternatives in rows and criteria in columns.

weights

Numeric weight vector, one per criterion. Renormalised to sum to 1 before use.

criteria_types

Character vector of "benefit" or "cost", one per criterion.

method

Ranking method. One of "topsis", "vikor", "moora", "smart", "waspas", "promethee", or "electre".

delta

Perturbation size as a proportion of the weight, default 0.05. A weight of 0.40 with delta = 0.05 is tested at 0.42 and 0.38 before renormalisation.

alternatives

Optional labels for the rows of x.

criteria

Optional labels for the columns of x.

...

Passed to the underlying method, for example v for VIKOR or lambda for WASPAS.

Value

An object of class sframe_sensitivity, a list with $table (one row per criterion and direction, carrying criterion, direction, rho, rank_changed, and top_changed), $base_ranks, $method, $delta, and $stable, a single logical that is TRUE when no perturbation changed the ranking.

See also

run_analysis_plan(), sframe_decision_options()

Examples

x <- matrix(c(4.1, 3.0, 210, 3.6, 4.5, 180, 4.8, 2.5, 260),
            nrow = 3, byrow = TRUE)
sa <- sensitivity_analysis(
  x,
  weights        = c(0.4, 0.3, 0.3),
  criteria_types = c("benefit", "benefit", "cost"),
  method         = "topsis",
  alternatives   = c("Alpha", "Basilica", "Coral"),
  criteria       = c("service", "location", "price")
)
sa$stable
#> [1] TRUE
as.data.frame(sa)
#>   criterion direction weight rho rank_changed top_changed
#> 1   service        up 0.4118   1        FALSE       FALSE
#> 2   service      down 0.3878   1        FALSE       FALSE
#> 3  location        up 0.3103   1        FALSE       FALSE
#> 4  location      down 0.2893   1        FALSE       FALSE
#> 5     price        up 0.3103   1        FALSE       FALSE
#> 6     price      down 0.2893   1        FALSE       FALSE