ZINB: Fit Zero-Inflated Negative Binomial Model with Arbitrary...

In sparsesurv: Forecasting and Early Outbreak Detection for Sparse Count Data

Fit Zero-Inflated Negative Binomial Model with Arbitrary Covariates and Prediction

Description

Fits a zero-inflated negative binomial (ZINB) model using JAGS, with an optional design matrix of covariates and full inprod for mean structure, and can generate posterior predictive counts for new covariate data.

Usage

ZINB(
  cases,
  pop = NULL,
  covariates_count = NULL,
  covariates_zero = NULL,
  covariatespred_count = NULL,
  covariatespred_zero = NULL,
  poppred = NULL,
  casespred = NULL,
  beta_init = NULL,
  delta_init = NULL,
  r_init = NULL,
  beta_prior_mean = 0,
  beta_prior_sd = 10,
  delta_prior_mean = 0,
  delta_prior_sd = 10,
  r_prior_shape = 1,
  r_prior_rate = 1,
  n_iter = 1e+05,
  n_burnin = 10000,
  n_chains = 3,
  n_thin = 1,
  save_params = c("beta", "delta", "r")
)

Arguments

cases	Vector of observed counts (length N)
pop	Optional vector of population offsets (length N)
covariates_count	Optional numeric matrix (N x P) of covariates for the count component.
covariates_zero	Optional numeric matrix (N x Q) of covariates for the zero-inflation component.
covariatespred_count	Optional numeric matrix (M x P) of new covariates for count prediction.
covariatespred_zero	Optional numeric matrix (M x Q) of new covariates for zero-inflation prediction.
poppred	Optional vector of population offsets (length M) for prediction.
casespred	Optional vector of true counts (length M) for prediction performance.
beta_init	Optional list of length n_chains for beta, count coefficients initial values.
delta_init	Optional list of length n_chains for delta, zero-inflation coefficients.
r_init	Optional numeric vector of length n_chains for dispersion parameter.
beta_prior_mean	Mean for beta prior (default: 0)
beta_prior_sd	SD for beta prior (default: 10)
delta_prior_mean	Mean for delta prior (default: 0)
delta_prior_sd	SD for delta prior (default: 10)
r_prior_shape	Shape for r ~ dgamma (default: 1)
r_prior_rate	Rate for r ~ dgamma (default: 1)
n_iter	Total MCMC iterations (default: 100000)
n_burnin	Burn-in iterations (default: 10000)
n_chains	Number of chains (default: 3)
n_thin	Thinning interval (default: 1)
save_params	Character vector of parameters to save (default c("beta","delta","r"))

Value

A list with MCMC summary, samples, DIC, and if prediction data provided: pred_matrix, pred_mean, mae, rmse

Examples

# ---- tiny example for users & CRAN (< 5s) ----
set.seed(5)
n <- 100
base  <- rnbinom(n, size = 5, mu = 7)
zeros <- rbinom(n, 1, 0.25)       # add extra zeros
cases <- ifelse(zeros == 1, 0L, base)


# ---- actually fit the model, but only when JAGS is available ----

fit <- ZINB(
  cases = cases,
  # optional priors if your API exposes them, e.g.:
  # beta_prior_mean = 0, beta_prior_sd = 5,
  # r_prior_shape = 2,  r_prior_rate  = 0.5,
  n_iter   = 400,            # keep fast
  n_burnin = 200,
  n_chains = 1,
  n_thin   = 2
)
print(fit)


# ---- longer user-facing demo (skipped on checks) ----
if (nzchar(Sys.which("jags")) && requireNamespace("R2jags", quietly = TRUE)) {
  x <- sin(2*pi*seq_len(n)/12)       # simple seasonal regressor
  fit2 <- ZINB(
    cases = cases,
    covariates_count   = cbind(x),
    covariates_zero = cbind(x),
    n_iter   = 1000,
    n_burnin = 100,
    n_chains = 2,
    n_thin   = 2
  )
  print(fit2)
  # if a plot method exists:  # plot(fit2)
}


## Not run: 
# ---- time-consuming / full demo ----
if (nzchar(Sys.which("jags")) && requireNamespace("R2jags", quietly = TRUE)) {
  fit_full <- ZINB(
    cases = cases,
    n_iter   = 100000,
    n_burnin = 10000,
    n_chains = 4,
    n_thin   = 5
  )
  print(fit_full)
}

## End(Not run)

if (interactive()) {
  # e.g., plot(fit)
}

sparsesurv documentation built on Sept. 11, 2025, 9:11 a.m.