fHMM_model: Constructor of a model object
In fHMM: Fitting Hidden Markov Models to Financial Data

fHMM_model

R Documentation

Constructor of a model object

Description

This function constructs an object of class fHMM_model, which contains details about the fitted (hierarchical) Hidden Markov model.

Usage

fHMM_model(
  data,
  estimate,
  nlm_output,
  estimation_time,
  ll,
  lls,
  gradient,
  inverse_fisher,
  decoding
)

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

## S3 method for class 'fHMM_model'
residuals(object, ...)

## S3 method for class 'fHMM_model'
summary(object, alpha = 0.05, ...)

## S3 method for class 'fHMM_model'
coef(object, alpha = 0.05, digits = 2, ...)

## S3 method for class 'fHMM_model'
AIC(object, ..., k = 2)

## S3 method for class 'fHMM_model'
BIC(object, ...)

## S3 method for class 'fHMM_model'
nobs(object, ...)

## S3 method for class 'fHMM_model'
logLik(object, ...)

npar(object, ...)

## S3 method for class 'fHMM_model'
npar(object, ...)

## S3 method for class 'fHMM_model'
predict(object, ahead = 5, alpha = 0.05, ...)

Arguments

`data`	[`fHMM_data`] An object of class `fHMM_data`.
`estimate`	[`numeric()`] A `numeric` vector of unconstrained model estimates.
`nlm_output`	[`list()`] The output of `nlm` for the selected optimization run.
`estimation_time`	[`difftime`] A `diff.time` object, the total estimation time.
`ll`	[`numeric(1)`] A `numeric`, the model log-likelihood.
`lls`	[`numeric()`] A `numeric` vector, the model log-likelihoods in all optimization runs.
`gradient`	[`numeric()`] A `numeric` vector, the gradient at the optimum.
`inverse_fisher`	[`numeric()`] A `numeric` vector, the inverse Fisher information for each parameter.
`decoding`	[`NULL` \| `numeric()` \| `matrix()`] A `numeric` vector, the decoded time series.
`x`, `object`	[`fHMM_model`] An object of class `fHMM_model`.
`...`	Currently not used.
`alpha`	[`numeric(1)`] A `numeric` between 0 and 1, the confidence level.
`digits`	[`integer(1)`] The number of decimal places.
`k`	[`numeric(1)`] Passed on to `AIC`.
`ahead`	[`integer(1)`] The number of time points to predict ahead.