POT (version 1.1-7)

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(fitted, plot = TRUE, …)

Arguments

fitted

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.

Value

A Markov chain which has the same features as the fitted object. If plot = TRUE, the Markov chain is plotted.

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 fitted;

2. For all prob such as \(prob \leq 1 - pat\), set \(mc = NA\) (where pat is given by fitted$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 fitted$threshold, fitted$param["scale"] and fitted$param["shape"] respectively.

See Also

fitmcgpd, simmc

Examples

# NOT RUN {
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")
# }