Skip to contents

Simulate data

Source: R/simulate_hmm.R
simulate_hmm.Rd

This helper function simulates HMM data.

Usage

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 = NA,
  true_parameters = fHMM_parameters(controls = controls, hierarchy = hierarchy, states =
    states, sdds = sdds),
  seed = NULL
)

Arguments

controls

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.

Important: Specifications in controls always override individual specifications.

hierarchy

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.

states

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.

sdds

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.

horizon

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.

period

Only relevant if hierarchy = TRUE.

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 = NA. If period is not NA, it overrules horizon[2].

true_parameters

An object of class fHMM_parameters, used as simulation parameters. By default, true_parameters = NULL, i.e., sampled true parameters.

seed

Set a seed for the data simulation. No seed per default.

Value

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

Examples

simulate_hmm(states = 2, sdds = "normal", horizon = 10)
#> $time_points
#>  [1]  1  2  3  4  5  6  7  8  9 10
#> 
#> $markov_chain
#>  [1] 2 2 2 1 1 1 1 1 1 1
#> 
#> $data
#>  [1] -0.6585950  1.0541351 -0.3196731 -0.2888282 -0.1801663 -0.3192822
#>  [7] -0.1275880 -0.2889544 -0.3983131 -0.1707813
#> 
#> $T_star
#> NULL
#>