Simulate an Markov Chain with a Fixed Extreme Value Dependence from a Fitted mcpot Object
Simulate a synthetic Markov chain from a fitted 'mcpot' object.
simmcpot(object, plot = TRUE, ...)
The simulated Markov chain is computed as follows:
Simulate a Markov chain prob with uniform margins on
(0,1) and with the fixed extreme value dependence given by
object;
For all prob such as prob <= 1
- pat, set mc = NA (where pat is given by
object$pat);
For all prob such as prob >= 1
- pat, set prob2 = (prob
- 1 + pat) / pat. Thus, prob2 are uniformly distributed on
(0,1);
For all prob2, set mc = qgpd(prob2, thresh,
scale, shape), where thresh, scale, shape are given by the
object$threshold, object$param["scale"] and
object$param["shape"] respectively.
A Markov chain which has the same features as the fitted object. If
plot = TRUE, the Markov chain is plotted.
Mathieu Ribatet
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")
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