This function simulates a time series from the distribution estimated by the given covlmc object.
Usage
# S3 method for covlmc
simulate(object, nsim = 1, seed = NULL, covariate, init = NULL, ...)Arguments
- object
a fitted covlmc object.
- nsim
length of the simulated time series (defaults to 1).
- seed
an optional random seed (see the dedicated section).
- covariate
values of the covariates.
- init
an optional initial sequence for the time series.
- ...
additional arguments.
Value
a simulated discrete time series of the same type as the one used to
build the covlmc with a seed attribute (see the Random seed section). The
results has also the dts class to hide the seed attribute when using
print or similar function.
Details
A VLMC with covariates model needs covariates to compute its transition
probabilities. The covariates must be submitted as a data frame using the
covariate argument. In addition, the time series can be initiated by a
fixed sequence specified via the init parameter.
Extended contexts
As explained in details in loglikelihood.covlmc() documentation and in
the dedicated vignette("likelihood", package = "mixvlmc"), the first
initial values of a time series do not in general have a proper context for
a COVLMC with a non zero order. In order to simulate something meaningful
for those values, we rely on the notion of extended context defined in the
documents mentioned above. This follows the same logic as using
loglikelihood.covlmc() with the parameter initial="extended". All
covlmc functions that need to manipulate initial values with no proper
context use the same approach.
Random seed
This function reproduce the behaviour of stats::simulate(). If seed is
NULL the function does not change the random generator state and returns
the value of .Random.seed as a seed attribute in the return value. This
can be used to reproduce exactly the simulation results by setting
.Random.seed to this value. Notice that if the random seed has not be
initialised by R so far, the function issues a call to runif(1) to
perform this initialisation (as is done in stats::simulate()).
It seed is an integer, it is used in a call to set.seed() before the
simulation takes place. The integer is saved as a seed attribute in the
return value. The integer seed is completed by an attribute kind which
contains the value as.list([RNGkind()]) exactly as with
stats::simulate(). The random generator state is reset to its original
value at the end of the call.
See also
stats::simulate() for details and examples on the random number generator setting
Examples
pc <- powerconsumption[powerconsumption$week == 5, ]
dts <- cut(pc$active_power, breaks = c(0, quantile(pc$active_power, probs = c(0.5, 1))))
dts_cov <- data.frame(day_night = (pc$hour >= 7 & pc$hour <= 17))
m_cov <- covlmc(dts, dts_cov, min_size = 5)
# new week with day light from 6:00 to 18:00
new_cov <- data.frame(day_night = rep(c(rep(FALSE, 59), rep(TRUE, 121), rep(FALSE, 60)), times = 7))
new_dts <- simulate(m_cov, nrow(new_cov), seed = 0, covariate = new_cov)
new_dts_2 <- simulate(m_cov, nrow(new_cov), seed = 0, covariate = new_cov, init = dts[1:10])