Model fitting — fit_model • fHMM

This function fits a HMM to fHMM_data via numerical likelihood maximization.

Usage

fit_model(data, ncluster = 1, seed = NULL, verbose = TRUE, init = NULL)

Arguments

data: An object of class fHMM_data.
ncluster: Set the number of clusters for parallelization. By default, ncluster = 1.
seed: Set a seed for the sampling of initial values. No seed by default.
verbose: Set to TRUE to print progress messages.
init: Optionally an object of class parUncon for initialization. This can for example be the estimate of a previously fitted model model, i.e. the element model$estimate. The initial values are computed via replicate(n, jitter(init, amount = 1), simplify = FALSE), where n <- data$controls$fit$runs.

Value

An object of class fHMM_model.

Details

The function is parallelized if ncluster > 1.

Examples

### 2-state HMM with normal distributions

# define model
controls <- set_controls(states = 2, sdds = "normal", horizon = 100, runs = 20)

# define parameters
parameters <- fHMM_parameters(controls, mu = c(-1, 1), seed = 1)

# sample data
data <- prepare_data(controls, true_parameter = parameters, seed = 1)

# fit model
model <- fit_model(data, seed = 1)
#> Checking start values
#> Maximizing likelihood
#> Computing Hessian
#> Fitting completed

# inspect fit
summary(model)
#> Summary of fHMM model
#> 
#>   simulated hierarchy        LL     AIC     BIC
#> 1      TRUE     FALSE -62.46398 136.928 152.559
#> 
#> State-dependent distributions:
#> normal() 
#> 
#> Estimates:
#>                 lb estimate      ub    true
#> Gamma_2.1  0.07189   0.1360  0.2423  0.1632
#> Gamma_1.2  0.14611   0.2643  0.4299  0.3116
#> mu_1      -1.01493  -0.9813 -0.9477 -1.0000
#> mu_2       0.91994   1.0404  1.1609  1.0000
#> sigma_1    0.08027   0.1015  0.1283  0.1008
#> sigma_2    0.41670   0.4953  0.5887  0.6008
plot(model, "sdds")


# decode states
model <- decode_states(model)
#> Decoded states

# predict
predict(model, ahead = 5)
#>   state_1 state_2       lb estimate      ub
#> 1 0.13598 0.86402  0.03891  0.76552 1.49212
#> 2 0.21754 0.78246 -0.07314  0.60063 1.27441
#> 3 0.26645 0.73355 -0.14035  0.50174 1.14383
#> 4 0.29579 0.70421 -0.18066  0.44243 1.06551
#> 5 0.31339 0.68661 -0.20483  0.40685 1.01854