plot_sensitivity: Plotting Sensitivities of a Stressed Model
In spesenti/SWIM: Scenario Weights for Importance Measurement

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

Plots the sensitivity measures for components (random variables) of a stochastic model under the scenario weights.

plot_sensitivity(
  object,
  xCol = "all",
  wCol = "all",
  type = c("Gamma", "Kolmogorov", "Wasserstein", "reverse", "all"),
  f = NULL,
  k = NULL,
  s = NULL,
  displ = TRUE,
  p = 1
)

`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", "Kolmogorov", "Wasserstein", "reverse", "all"` (`default = "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"`.
`displ`	Logical, if `TRUE` the plot is displayed, otherwise the data.frame for customised plotting with `ggplot` is returned (`default = TRUE`).
`p`	Numeric vector, the p-th moment of Wasserstein distance (`default = 1`).

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

Note that the Kolmogorov distance is the same for all inputs under the same stress for a SWIM object. Thus, it should only be used to compare different stresses, not individual components.

If displ = TRUE, a plot displaying the sensitivity measures of the stochastic model under the scenario weights. If displ = FALSE, a data.frame for customised plotting with ggplot. The data.frame contains the columns: stress (the stresses), type (the types of sensitivity), X_all (the random variables), value (the values of the sensitivities).
Denote by result the return of the function call, then ggplot can be called via:

ggplot(result, aes(x = X_{all}, y = value))

+ geom_{point}(aes(color = factor(stress), shape = type)).

Silvana M. Pesenti

See sensitivity for the values of the sensitivity measures of a stressed model and importance_rank for ranking of random variables according to their sensitivities.

## Consider the portfolio Y = X1 + X2 + X3 + X4 + X5,
## where (X1, X2, X3, X4, X5) are correlated normally
## distributed with equal mean and different standard deviations,
## see the README for further details.


set.seed(0)
SD <- c(70, 45, 50, 60, 75)
Corr <- matrix(rep(0.5, 5 ^ 2), nrow = 5) + diag(rep(1 - 0.5, 5))
if (!requireNamespace("mvtnorm", quietly = TRUE))
   stop("Package \"mvtnorm\" needed for this function
   to work. Please install it.")
x <- mvtnorm::rmvnorm(10 ^ 5,
   mean =  rep(100, 5),
   sigma = (SD %*% t(SD)) * Corr)
data <- data.frame(rowSums(x), x)
names(data) <- c("Y", "X1", "X2", "X3", "X4", "X5")
rev.stress <- stress(type = "VaR", x = data,
   alpha = c(0.75, 0.9), q_ratio = 1.1, k = 1)

sensitivity(rev.stress, type = "all")
plot_sensitivity(rev.stress, xCol = 2:6, type = "Gamma")
plot_sensitivity(rev.stress, xCol = 6, wCol = 1, type = "all")