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Set and check model parameters

Source: R/fHMM_parameters.R
fHMM_parameters.Rd

This function sets and checks model parameters. Unspecified parameters are sampled.

Usage

fHMM_parameters(
  controls = list(),
  hierarchy = FALSE,
  states = if (!hierarchy) 2 else c(2, 2),
  sdds = if (!hierarchy) "normal" else c("normal", "normal"),
  Gamma = NULL,
  mu = NULL,
  sigma = NULL,
  df = NULL,
  Gamma_star = NULL,
  mu_star = NULL,
  sigma_star = NULL,
  df_star = NULL,
  scale_par = c(1, 1),
  seed = NULL,
  check_controls = TRUE
)

# S3 method for class 'fHMM_parameters'
print(x, ...)

Arguments

controls

[list() | fHMM_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 a 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

[logical(1)]
A logical, set to TRUE for a 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

[integer(1) | integer(2)]
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

[character(1) | character(2)]
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.

Gamma, Gamma_star

[NULL | matrix() | list()]
A transition probability matrix.

It should have dimension states[1].

Gamma_star is a list of fine-scale transition probability matrices. The list must be of length states[1]. Each transition probability matrix must be of dimension states[2].

mu, mu_star

[NULL | numeric() | list()]
A numeric vector of expected values for the state-dependent distribution in the different states.

For the gamma- or Poisson-distribution, mu must be positive.

It should have length states[1].

mu_star is a list of vectors with fine-scale expectations. The list must be of length states[1]. Each vector must be of length states[2].

sigma, sigma_star

[NULL | numeric() | list()]
A positive numeric vector of standard deviations for the state-dependent distribution in the different states.

It should have length states[1].

sigma_star is a list of vectors with fine-scale standard deviations. The list must be of length states[1]. Each vector must be of length states[2].

df, df_star

[NULL | numeric() | list()]
A positive numeric vector of degrees of freedom for the state-dependent distribution in the different states.

It should have length states[1].

Only relevant in case of a state-dependent t-distribution.

df_star is a list of vectors with fine-scale degrees of freedom. The list must be of length states[1]. Each vector must be of length states[2]. Only relevant in case of a fine-scale state-dependent t-distribution.

scale_par

[numeric(2)]
A positive numeric vector of length two, containing scales for sampled expectations and standard deviations.

The first entry is the scale for mu and sigma, the second entry is the scale for mu_star and sigma_star (if any).

seed

[NULL | integer(1)]
Sets a seed for the sampling of parameters.

check_controls

[logical(1)]
Either TRUE to check the defined controls or FALSE to not check them (which saves computation time), else.

x

[fHMM_parameters]
An object of class fHMM_parameters.

...

Currently not used.

Value

An object of class fHMM_parameters.

Details

See the package vignettes at https://loelschlaeger.de/fHMM/articles/ for details.

Examples

parameters <- fHMM_parameters(states = 2, sdds = "normal")
parameters$Gamma
#>           state_1   state_2
#> state_1 0.7265505 0.2734495
#> state_2 0.1083762 0.8916238