get_data: Extracting from a Stressed Model
In spesenti/SWIM: Scenario Weights for Importance Measurement

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

Extracting the data (realisations of the stochastic model), the scenario weights, the functions generating the scenario weights, or the specifications of the stress from an object of class SWIM or SWIMw.

get_data(object, xCol = "all")

get_weights(object, wCol = "all")

get_weightsfun(object, wCol = "all")

get_specs(object, wCol = "all")

summary_weights(object, wCol = "all")

`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"`).

get_data: A data.frame containing the realisations of the stochastic model on which the object is based.

get_weights: A data.frame containing the scenario weights of the object. Columns corresponds to different stresses.

get_weightsfun: A list containing functions, which, when applied to a column of the data, generate the scenario weights of the object. The corresponding stressed columns can be obtained via get_specs.

Use get_weights if the SWIM object only contains scenario weights and not a list of functions.

get_specs: A data.frame containing specifications of the stresses with each row corresponding to a different stress. Only a selection of the specifications is returned; however, all input variables are stored in the object. See also SWIM.

summary_weights: print a list containing summary statistics of the stresses with each element being a table for a different stress. The summary statistics include minimum, maximum, standard deviation, Gini coefficient, entropy and effective sample size.

Gini coefficient uses the formula \frac{∑_{i=1}^{n} ∑_{j=1}^{n}≤ft|x_{i}-x_{j}\right|}{2 n^{2} \bar{x}}.

Effective Sample Size is equal to n / (1+Var(W)), see Equation (9.13) in Owen, Art B. "Monte Carlo theory, methods and examples." (2013).

get_data: extracting data.
get_weights: extracting scenario weights.
get_weightsfun: extracting weight functions.
get_specs: extracting information of the stress.
summary_weights: extracting summary statistics of scenario weights.

Silvana M. Pesenti

SWIM

## continuing example in stress_VaR
set.seed(0)
x <- as.data.frame(cbind(
     "normal" = rnorm(1000), 
     "gamma" = rgamma(1000, shape = 2)))
  res1 <- stress(type = "VaR", x = x, 
                 alpha = 0.9, q_ratio = 1.05, k = 1)

## returning the underlying data
all(get_data(res1) == x)
 ## the scenario weights
w <- get_weights(res1) 
get_weightsfun(res1)
get_specs(res1)
  
## now add a stress on the means of both variables
res1 <- stress(type = "mean", x = res1, k = 1:2, new_means = c(0.5,1.5))
get_specs(res1)
## the required moments for a stress of type "mean" are not displayed 
## the type of stress and the specs for the second stress can be 
## extracted directly from the SWIM object.
res1$type[[2]]
res1$specs[[2]]