stress_mean: Stressing Means
In spesenti/SWIM: Scenario Weights for Importance Measurement

Description Usage Arguments Details Value References See Also Examples

Provides weights on simulated scenarios from a baseline stochastic model, such that stressed model components (random variables) fulfil the mean constraints. Scenario weights are selected by constrained minimisation of the relative entropy to the baseline model.

1	stress_mean(x, k, new_means, normalise = TRUE, names = NULL, log = FALSE, ...)

`x`	A vector, matrix or data frame containing realisations of random variables. Columns of `x` correspond to random variables; OR A `SWIM` object, where `x` corresponds to the underlying data of the `SWIM` object.
`k`	Numeric vector, the columns of `x` that are stressed.
`new_means`	Numeric vector, same length as `k`, containing the stressed means.
`normalise`	Logical. If true, values of `f(x)` are linearly scaled to the unit interval.
`names`	Character vector, the names of stressed models.
`log`	Boolean, the option to print weights' statistics.
`...`	Additional arguments to be passed to `nleqslv` or `stress_moment`.

The function stress_mean is a wrapper for the function stress_moment. See stress_moment for details on the additional arguments to ... and the underlying algorithm.

A SWIM object containing:

x, a data.frame containing the data;
new_weights, a list, each component corresponds to a different stress and is a vector of scenario weights;
type = "mean";
specs, a list, each component corresponds to a different stress and contains k and new_means.

See SWIM for details.

\insertRef

Pesenti2019reverseSWIM

\insertRef

Pesenti2020SSRNSWIM

\insertRef

Csiszar1975SWIM

See stress_mean_sd for stressing means and standard deviations jointly, and stress_moment for moment constraints.

Other stress functions: stress_HARA_RM_w(), stress_RM_mean_sd_w(), stress_RM_w(), stress_VaR_ES(), stress_VaR(), stress_mean_sd_w(), stress_mean_sd(), stress_mean_w(), stress_moment(), stress_prob(), stress_user(), stress_wass(), stress()

set.seed(0)
x <- data.frame(cbind(
  "normal" = rnorm(1000),
  "gamma" = rgamma(1000, shape = 2),
  "beta" = rbeta(1000, shape1 = 2, shape2 = 2)))
## stressing means
res1 <- stress(type = "mean", x = x, k = 1:3,
  new_means = c(1, 1, 0.75))
summary(res1)
res1$specs
## calling stress_mean directly
res2 <- stress_mean(x = x, k = 1:3,
  new_means = c(1, 1, 0.75))
summary(res2)

## See also examples in stress_moment and stress_mean_sd.