POT (version 1.1-11)

simmcpot: Simulate an Markov Chain with a Fixed Extreme Value Dependence from a Fitted mcpot Object

Description

Simulate a synthetic Markov chain from a fitted 'mcpot' object.

Usage

simmcpot(object, plot = TRUE, ...)

Value

A Markov chain which has the same features as the fitted object. If

plot = TRUE, the Markov chain is plotted.

Arguments

object

An object of class 'mcpot'; most often the returned object of the fitmcgpd function.

plot

Logical. If TRUE (the default), the simulated Markov chain is plotted.

...

Other optional arguments to be passed to the plot function.

Author

Mathieu Ribatet

Details

The simulated Markov chain is computed as follows:

1. Simulate a Markov chain prob with uniform margins on (0,1) and with the fixed extreme value dependence given by object;

2. For all prob such as \(prob \leq 1 - pat\), set \(mc = NA\) (where pat is given by object$pat);

3. For all prob such as \(prob \geq 1 - pat\), set \(prob2 = \frac{prob - 1 + pat}{pat}\). Thus, prob2 are uniformly distributed on (0,1);

4. For all prob2, set mc = qgpd(prob2, thresh, scale, shape), where thresh, scale, shape are given by the object$threshold, object$param["scale"] and object$param["shape"] respectively.

See Also

fitmcgpd, simmc

Examples

data(ardieres)
flows <- ardieres[,"obs"]

Mclog <- fitmcgpd(flows, 5)
par(mfrow = c(1,2))
idx <- which(flows <= 5)
flows[idx] <- NA
plot(flows, main = "Ardieres Data")
flowsSynth <- simmcpot(Mclog, main = "Simulated Data")