plot_cdf {SWIM}

Plotting the Distribution Functions of a Stressed Model

Description

Plots the empirical distribution function of a stressed SWIM model component (random variable) or KDE distribution function of a stressed SWIMw model component under the scenario weights.

Usage

plot_cdf(
  object,
  xCol = 1,
  wCol = "all",
  base = FALSE,
  n = 500,
  x_limits,
  y_limits,
  displ = TRUE
)

Arguments

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").

base

Logical, if TRUE, statistics under the baseline are also returned (default = "FALSE").

n

Integer, the number of points used to plot stat_ecdf in ggplot (default = 500).

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).

Value

If displ = TRUE, a plot displaying the empirical or KDE distribution function of the stochastic model under the scenario weights.

If displ = FALSE, a data.frame for customised plotting with ggplot. The data.frame contains the columns: the column, xCol, of the data of the stressed model, stress (the stresses) and value (the values).
Denote by res the return of the function call, then ggplot can be called via:

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

+ stat_{ecdf}(aes(color = factor(stress)), n = n).

Note that the ggplot2 default of stat_ecdf does not take weight as an aesthetic. We use the workaround by Nicolas Woloszko, see Note below.

Note

This function is based on the ggplot stat_ecdf function. However, the stat_ecdf does not allow for specifying weights, thus the function is based on the workaround by Nicolas Woloszko, see https://github.com/NicolasWoloszko/stat_ecdf_weighted.

Author(s)

Silvana M. Pesenti, Zhuomin Mao

See Also

See cdf for the empirical or KDE distribution function of a stressed model and quantile_stressed for sample quantiles of a stressed model.

Examples

## example with a stress on VaR
set.seed(0)
x <- as.data.frame(cbind(
  "normal" = rnorm(10 ^ 5),
  "gamma" = rgamma(10 ^ 5, shape = 2)))
res1 <- stress(type = "VaR", x = x,
  alpha = c(0.75, 0.95), q_ratio = 1.15)
plot_cdf(res1, xCol = 1, wCol = 1:2, base = TRUE)
plot_cdf(res1, xCol = 1, wCol = 1:2, base = TRUE,
  x_limits = c(0, 5), y_limits = c(0.5, 1))