sim.mar1s {mar1s} | R Documentation |
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.
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, ...)
object |
An object of class "mar1s" specifying the model parameters.
|
n.ahead |
Number of steps ahead at which to simulate/predict. |
n.sim |
Number of simulations. |
start.time |
The sampling time for the first simulation step. |
xreg.absdata |
A matrix-like object with row count = n.ahead , specifying the
values for the external regressors. If NULL , default values
are used.
|
init.absdata |
A vector specifying the initial values of the process. If
NULL , default values are used.
|
probs |
A vector of probabilities. |
... |
Arguments from previous methods. |
For sim.mar1s
, a vector of simulated values.
For predict.mar1s
, a vector of estimated quantiles.
compose.mar1s
for MAR(1)S process formal definition and
composition/decomposition functions, fit.mar1s
for
fitting MAR(1)S process to data.
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))