quantile_stressed: Sample Quantiles of a Stressed Model
In spesenti/SWIM: Scenario Weights for Importance Measurement

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

Provides sample quantiles for components (random variables) of a stochastic model, corresponding to distribution functions under the scenario weights.

quantile_stressed(
  object,
  probs = seq(0, 1, 0.25),
  xCol = "all",
  wCol = 1,
  type = c("quantile", "(i-1)/(n-1)", "i/(n+1)", "i/n"),
  base = FALSE
)

`object`	A `SWIM` or `SWIMw` object.
`probs`	Vector of probabilities with values in `[0,1]` (`default = (0, 0.25, 0.5, 0.75, 1)`).
`xCol`	Numeric or character vector, (names of) the columns of the underlying data of the `object` (`default = "all"`).
`wCol`	Numeric, the column of the scenario weights of the `object` (`default = 1`).
`type`	Character, one of `"quantile","(i-1)/(n-1)", "i/(n+1)","i/n"`, (`default = "quantile"`). See details below.
`base`	Logical, if `TRUE`, statistics under the baseline are also returned (`default = "FALSE"`).

type defines the choice of algorithm used for calculating the estimate of the sample quantiles. "quantile" corresponds to the default interpolation used in quantile. Further options are "(i-1)/(n-1)", "i/(n+1)", "i/n" the inverse of the empirical distribution function, using, respectively, (wt - 1)/T, wt/(T+1), wt/T, where wt is the cumulative weight and T the total weight (usually total sample size). See wtd.quantile for further details on type, on which quantile_stressed is based. type is ignored for when evaluating quantiles for SWIMw objects.

Returns a matrix with estimates of the distribution quantiles at the probabilities, probs, under the scenario weights wCol.

Silvana M. Pesenti, Zhuomin Mao

See wtd.quantile on which the function quantile_stressed is based.
See cdf for the empirical distribution function of a stressed model.

     
## example with a stress on VaR
set.seed(0)
x <- as.data.frame(cbind(
  "normal" = rnorm(1000), 
  "gamma" = rgamma(1000, shape = 2)))
res1 <- stress(type = "VaR", x = x, 
  alpha = c(0.9, 0.95), q_ratio = 1.05)
## stressed sample quantiles  
quantile_stressed(res1, probs = seq(0.9, 0.99, 0.01), wCol = 2)