Package {ZIHINAR1}

Simulate Time Series Data for ZI-INAR(1) or H-INAR(1) Models

Description

This function simulates time series data for Zero-Inflated INAR(1) (ZI-INAR(1)) or Hurdle INAR(1) (H-INAR(1)) models, using Poisson, Negative Binomial, Generalized Poisson, or Conway-Maxwell-Poisson (CMP) innovations.

Usage

data_simu(n, alpha, rho, lambda, disp = NULL, mod_type, distri)

Arguments

Details

The first 100 observations are discarded as burn-in. Zero inflation applies to the innovations, not directly to the series. For an ordinary CMP-INAR(1) process use rho = 0. This simulates CMP innovations, not a CMP stationary marginal distribution.

Value

A numeric vector containing the simulated time series data.

References

Part of the implementation of this function was adapted from the ZINAR1 package. The ZINAR1 package simulates first-order integer-valued autoregressive processes with zero-inflated innovations (ZINAR(1)) and estimates its parameters under a frequentist approach.

For more information about the ZINAR1 package, please refer to:

Aldo M. Garay, João Vitor Ribeiro (2022). ZINAR1: Simulates ZINAR(1) Model and Estimates Its Parameters Under Frequentist Approach. R package version 0.1.0. Available at: https://CRAN.R-project.org/package=ZINAR1.

Garay, A. M., Ribeiro, J. V. (2021). First-Order Integer Valued AR Processes with Zero-Inflated Innovations. In: Nonstationary Systems: Theory and Applications, Springer. DOI: doi:10.1007/978-3-030-82110-4_2.

We acknowledge the original authors, Aldo M. Garay and João Vitor Ribeiro, for their contributions.

Examples

# Simulate 50 observations from a Zero-Inflated Poisson INAR(1) model
y_data <- data_simu(n = 50, alpha = 0.5, rho = 0.3, lambda = 5,
                    mod_type = "zi", distri = "poi")
head(y_data)

# Simulate a zero-inflated CMP-INAR(1) series (disp is nu)
y_cmp <- data_simu(n = 50, alpha = 0.5, rho = 0.3, lambda = 2.5,
                   disp = 1.2, mod_type = "zi", distri = "cmp")

Get Parameter Estimates from Stan Model Fit

Description

Extracts parameter estimates from a Stan model fit, including mean, median, standard deviation, and HPD intervals.

Usage

get_est(distri, stan_fit)

Arguments

Value

A summary of the parameter estimates.

Examples

  # Generate toy data
  y_data <- data_simu(n = 60, alpha = 0.5, rho = 0.3, lambda = 5,
                      mod_type = "zi", distri = "poi")

  # Fit a small Stan model (may take > 5s on first compile)
  stan_fit <- get_stanfit(mod_type = "zi", distri = "poi", y = y_data)

  # Get parameter estimates from the Stan model fit
  get_est(distri = "poi", stan_fit = stan_fit)

Get Model Selection Criteria

Description

Calculates model selection criteria such as AIC, BIC, DIC, and WAIC from a Stan model fit.

Usage

get_mod_sel(y, mod_type, distri, stan_fit, M = NULL)

Arguments

Value

A summary table of model selection criteria, including:

EAIC

Expected Akaike Information Criterion (AIC).

EBIC

Expected Bayesian Information Criterion (BIC).

DIC

Deviance Information Criterion (DIC).

WAIC1

First version of Watanabe-Akaike Information Criterion (WAIC).

WAIC2

Second version of Watanabe-Akaike Information Criterion (WAIC).

The criteria are returned in a one-row data frame.

Examples

  # Generate toy data
  y_data <- data_simu(n = 60, alpha = 0.5, rho = 0.3, lambda = 5,
                      mod_type = "zi", distri = "poi")

  # Fit a small Stan model (may take > 5s on first compile)
  stan_fit <- get_stanfit(mod_type = "zi", distri = "poi", y = y_data)

  # Get model selection criteria
  get_mod_sel(y = y_data, mod_type = "zi", distri = "poi",
              stan_fit = stan_fit)

Get Predictions from Stan Model Fit

Description

Extracts predicted values from a Stan model fit.

Usage

get_pred(stan_fit)

Arguments

Value

A summary of the predictions and bar charts of each prediction. If no forecasts were requested, the summary has zero rows and the plot list is empty.

Examples

  # Generate toy data
  y_data <- data_simu(n = 60, alpha = 0.5, rho = 0.3, lambda = 5,
                      mod_type = "zi", distri = "poi")

  # Fit a small Stan model (may take > 5s on first compile)
  stan_fit <- get_stanfit(mod_type = "zi", distri = "poi", y = y_data)

  # Get predicted values from the Stan model fit
  get_pred(stan_fit = stan_fit)

Fit ZI-INAR(1) or H-INAR(1) Model using Stan

Description

This function fits a Zero-Inflated INAR(1) (ZI-INAR(1)) or Hurdle INAR(1) (H-INAR(1)) model using Stan and returns the model fit.

Usage

get_stanfit(
  mod_type,
  distri,
  y,
  n_pred = 4,
  thin = 2,
  chains = 1,
  iter = 2000,
  warmup = iter/2,
  seed = NA,
  M = NULL,
  cmp_version = c("fast", "reference"),
  cmp_tail_tol = 1e-06,
  tail_action = c("warn", "error"),
  ...
)

Arguments

Value

A stanfit object containing the Stan model fit.

Examples

  # Generate toy data
  y_data <- data_simu(n = 60, alpha = 0.5, rho = 0.3, lambda = 5,
                      mod_type = "zi", distri = "poi")

  # Fit the model using Stan (small config)
  stan_fit <- get_stanfit(mod_type = "zi", distri = "poi", y = y_data)
  print(stan_fit)



  # Simulate and fit a zero-inflated CMP-INAR(1) series
  y_cmp <- data_simu(n = 60, alpha = 0.5, rho = 0.3, lambda = 2.5,
                     disp = 1.2, mod_type = "zi", distri = "cmp")
  fit_cmp <- get_stanfit(mod_type = "zi", distri = "cmp", y = y_cmp,
                         M = 300, cmp_version = "fast")
  print(fit_cmp)