importance_rank: Importance Ranking for a Stressed Model
In spesenti/SWIM: Scenario Weights for Importance Measurement

Description Usage Arguments Details Value Author(s) See Also Examples

Provides the importance ranks of the components (random variables) of a stressed model for different sensitivity measures.

importance_rank(
  object,
  xCol = "all",
  wCol = "all",
  type = c("Gamma", "Wasserstein", "reverse", "all"),
  f = NULL,
  k = NULL,
  s = NULL
)

`object`	A `SWIM` or `SWIMw` object.
`xCol`	Numeric or character vector, (names of) the columns of the underlying data of the `object` (`default = "all"`). If `xCol = NULL`, only the transformed data `f(x)` is considered.
`wCol`	Vector, the columns of the scenario weights of the `object` corresponding to different stresses (`default = "all"`).
`type`	Character, one of `"Gamma", "Wasserstein", "Kolmogorov", "reverse", "all"`.
`f`	A function, or list of functions, that, applied to `x`, constitute the transformation of the data for which the sensitivity is calculated.
`k`	A vector or list of vectors, same length as `f`, indicating which columns of `x` each function in `f` operates on. When `f` is a list, `k[[i]]` corresponds to the input variables of `f[[i]]`.
`s`	A function that, applied to `x`, defines the reverse sensitivity measure. If `type = "reverse"` and `s = NULL`, defaults to `type = "Gamma"`.

For the definition of the sensitivity measures (type), see sensitivity.

A data.frame containing the importance ranks of the stressed model for different sensitivity measures. Small values correspond to large sensitivities. Different rows correspond to different random variables. The first two rows specify the stress and type of the sensitivity measure on which the ranking is calculated.

Silvana M. Pesenti

See sensitivity for the values of the sensitivity measures, plot_sensitivity for plotting sensitivity measures and summary for a summary statistic of a stressed model.

## example with a stress on VaR
set.seed(0)
x <- as.data.frame(cbind(
  "log-normal" = rlnorm(1000),
  "gamma" = rgamma(1000, shape = 2)))
res1 <- stress(type = "VaR", x = x,
  alpha = c(0.9, 0.95), q_ratio = 1.05)

importance_rank(res1, wCol = 1:2, type = "Gamma")
## sensitivity of log-transformed data
importance_rank(res1, wCol = 1, type = "all",
  f = list(function(x)log(x), function(x)log(x)), k = list(1, 2))