Last data update: 2014.03.03
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R: Simulate from MAR(1)S Process
Simulate from MAR(1)S Process
Description
sim.mar1s simulates from MAR(1)S process.
predict.mar1s is a wrapper around sim.mar1s which
estimates confidence intervals for the future values of the MAR(1)S
process.
Usage
sim.mar1s(object, n.ahead = 1, n.sim = 1, start.time = 0,
xreg.absdata = NULL, init.absdata = NULL)
## S3 method for class 'mar1s'
predict(object, n.ahead = 1, start.time = 0,
xreg.absdata = NULL, init.absdata = NULL,
probs = c(0.05, 0.5, 0.95), n.sim = 1000, ...)
Arguments
object |
An object of class "mar1s" specifying the model parameters.
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n.ahead |
Number of steps ahead at which to simulate/predict.
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n.sim |
Number of simulations.
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start.time |
The sampling time for the first simulation step.
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xreg.absdata |
A matrix-like object with row count = n.ahead , specifying the
values for the external regressors. If NULL , default values
are used.
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init.absdata |
A vector specifying the initial values of the process. If
NULL , default values are used.
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probs |
A vector of probabilities.
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... |
Arguments from previous methods.
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Value
For sim.mar1s , a vector of simulated values.
For predict.mar1s , a vector of estimated quantiles.
See Also
compose.mar1s for MAR(1)S process formal definition and
composition/decomposition functions, fit.mar1s for
fitting MAR(1)S process to data.
Examples
data(forest.fire, package = "mar1s")
data(nesterov.index, package = "mar1s")
## Univariate
mar1s <- fit.mar1s(forest.fire)
sim.mar1s(mar1s)
sim.mar1s(mar1s, n.sim = 6)
sim.mar1s(mar1s, n.ahead = 3)
predict(mar1s)
predict(mar1s, n.ahead = 10)
predict(mar1s, init.absdata = 100)
t <- seq(1/12, 11/12, 1/6)
p <- mapply(predict, start.time = t,
MoreArgs = list(object = mar1s, probs = c(0.05, 0.95)))
plot(exp(mar1s$logseasonal), ylim = c(0, max(p)),
ylab = "Forest fire")
arrows(t, p[1, ], t, p[2, ],
code = 3, angle = 90, length = 0.05)
## External regressors
mar1s <- fit.mar1s(forest.fire, nesterov.index[, "mean"])
sim.mar1s(mar1s)
sim.mar1s(mar1s, n.sim = 6)
predict(mar1s)
predict(mar1s, xreg.absdata = 10000)
predict(mar1s, init.absdata = c(100, 1000))
Results
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