plot_weights: Plotting the scenario weights of a Stressed Model
In spesenti/SWIM: Scenario Weights for Importance Measurement

Description Usage Arguments Value See Also Examples

Plots the scenario weights of a stressed model against a model component.

plot_weights(
  object,
  xCol = 1,
  wCol = "all",
  n,
  x_limits,
  y_limits,
  displ = TRUE
)

`object`	A `SWIM` or `SWIMw` object.
`xCol`	Numeric or character, (name of) the column of the underlying data of the `object` (`default = 1`).
`wCol`	Vector, the columns of the scenario weights of the `object` corresponding to different stresses (`default = "all"`).
`n`	Integer, the number of points used to plot (`default = 5000` or the minimum of the data). If `n = "all"`, all data points are plotted. If `n` is a subset of points, the plotted scenario weights are chosen in an equidistant way.
`x_limits`	Vector, the limits of the x-axis of the plot, the value for `xlim` in the `coord_cartesian` function in `ggplot`.
`y_limits`	Vector, the limits of the y-axis of the plot, the value for `ylim` in the `coord_cartesian` function in `ggplot`.
`displ`	Logical, if `TRUE` the plot is displayed, otherwise the data.frame for customised plotting with `ggplot` is returned (`default = TRUE`).

If displ = TRUE, a plot displaying the scenario weights of a stochastic model against a model component.

If displ = FALSE, a data.frame for customised plotting with ggplot. The data.frame contains the following columns: grid, the grid points to plot the quantiles, stress (the stresses) and value (the quantile values).
Denote by res the return of the function call, then ggplot can be called via:

ggplot(res, aes(x = res[ ,1], y = value))

+ geom_lines(aes(color = factor(stress))).

See plot_quantile for plotting sample quantiles of a stressed model and plot_cdf for plotting empirical distribution functions.

 
## example with a stress with \code{credit_data} data set:
data("credit_data")
## two stresses in VaR
model_stress <- stress_VaR(credit_data, alpha = c(0.9, 0.95), q_ratio = 1.1, k =1) 
plot_weights(model_stress, xCol = "L", wCol = 1:2)

## additional stress on VaR and ES
model_stress <- stress_VaR_ES(model_stress, alpha = 0.9, q_ratio = 1.1, s_ratio = 1.2, k =1) 
plot_weights(model_stress, xCol = "L", wCol = "all", n = 1000, x_limits = c(0, 3500), 
             y_limits = c(0, 10))