cor_stressed: Correlation of a Stressed Model
In spesenti/SWIM: Scenario Weights for Importance Measurement

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

Provides the correlation of stressed model components (random variable) under the scenario weights.

cor_stressed(
  object,
  xCol = c(1, 2),
  wCol = "all",
  method = "Pearson",
  base = FALSE
)

`object`	A `SWIM` or `SWIMw` object.
`xCol`	Numeric or character vector, (names of) the columns of the underlying data of the `object` (`default = "all"`).
`wCol`	Vector, the columns of the scenario weights of the `object` corresponding to different stresses (`default = "all"`).
`method`	Character, one of `"Pearson", "Spearman", "Kendall"`. (`default = "Pearson"`).
`base`	Logical, if `TRUE`, statistics under the baseline are also returned (`default = "FALSE"`).

cor_stressed: The correlation coefficient of stressed model components, subject to the calculated scenario weights.

cor_stressed returns a list of correlation matrices corresponding to different stresses. Entries of the matrices denote correlation coefficients between stressed model components specified by xCol.

Kent Wu

See var_stressed and sd_stressed compute stressed variance and standard deviations under the scenario weights, respectively.

See cor for unweighted correlations between model components, while cor_stressed return correlations between stressed model components

     
## 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 correlation
cor_stressed(res1, xCol = c(1, 2), wCol = 1, base=TRUE)