This helper function simulates HMM data.
simulate_hmm(
controls = list(),
hierarchy = FALSE,
states = if (!hierarchy) 2 else c(2, 2),
sdds = if (!hierarchy) "normal" else c("normal", "normal"),
horizon = if (!hierarchy) 100 else c(100, 30),
period = if (hierarchy && is.na(horizon[2])) "m" else NA,
true_parameters = fHMM_parameters(controls = controls, hierarchy = hierarchy, states =
states, sdds = sdds),
seed = NULL
)A list containing the following elements:
time_points, the vector (or matrix in the
hierarchical case) of time points,
markov_chain, the vector (or matrix in the
hierarchical case) of the simulated states,
data, the vector (or matrix in the hierarchical
case) of the simulated state-dependent observations,
T_star, the numeric vector of fine-scale chunk sizes
in the hierarchical case
Either a list or an object of class fHMM_controls.
The list can contain the following elements, which are described
in more detail below:
hierarchy, defines an hierarchical HMM,
states, defines the number of states,
sdds, defines the state-dependent distributions,
horizon, defines the time horizon,
period, defines a flexible, periodic fine-scale time horizon,
data, a list of controls that define the data,
fit, a list of controls that define the model fitting
Either none, all, or selected elements can be specified.
Unspecified parameters are set to their default values, see below.
Specifications in controls override individual specifications.
A logical, set to TRUE for an hierarchical HMM.
If hierarchy = TRUE, some of the other controls must be specified for
the coarse-scale and the fine-scale layer.
By default, hierarchy = FALSE.
An integer, the number of states of the underlying Markov chain.
If hierarchy = TRUE, states must be a vector of length
2. The first entry corresponds to the coarse-scale layer, while the second
entry corresponds to the fine-scale layer.
By default, states = 2 if hierarchy = FALSE and
states = c(2, 2) if hierarchy = TRUE.
A character, specifying the state-dependent distribution. One of
"normal" (the normal distribution),
"lognormal" (the log-normal distribution),
"t" (the t-distribution),
"gamma" (the gamma distribution),
"poisson" (the Poisson distribution).
The distribution parameters, i.e. the
mean mu,
standard deviation sigma (not for the Poisson distribution),
degrees of freedom df (only for the t-distribution),
can be fixed via, e.g., "t(df = 1)" or
"gamma(mu = 0, sigma = 1)".
To fix different values of a parameter for different states, separate by
"|", e.g. "poisson(mu = 1|2|3)".
If hierarchy = TRUE, sdds must be a vector of length 2.
The first entry corresponds to the coarse-scale layer, while the second entry
corresponds to the fine-scale layer.
By default, sdds = "normal" if hierarchy = FALSE and
sdds = c("normal", "normal") if hierarchy = TRUE.
A numeric, specifying the length of the time horizon.
If hierarchy = TRUE, horizon must be a vector of length
2. The first entry corresponds to the coarse-scale layer, while the second
entry corresponds to the fine-scale layer.
By default, horizon = 100 if hierarchy = FALSE and
horizon = c(100, 30) if hierarchy = TRUE.
If data is specified (i.e., not NA), the first entry of
horizon is ignored and the (coarse-scale) time horizon is defined by
available data.
Only relevant if hierarchy = TRUE and horizon[2] = NA.
In this case, a character which specifies a flexible, periodic
fine-scale time horizon and can be one of
"w" for a week,
"m" for a month,
"q" for a quarter,
"y" for a year.
By default, period = "m" if hierarchy = TRUE and
horizon[2] = NA, and NA else.
An object of class fHMM_parameters, used as simulation parameters.
By default, true_parameters = NULL, i.e., sampled true parameters.
Set a seed for the data simulation. No seed per default.
simulate_hmm(states = 2, sdds = "normal", horizon = 10)
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