zero_poisson is used to fit zero-inflated
poisson regression models to count data via Bayesian inference.
zero_poisson(y, x, a = 1, b = 1, lam.start = 1, beta.prior.mean = 0,
beta.prior.sd = 1, iters = 1000, burn = 500, nthin = 1, plots = T,
progress.bar = T)numeric response vector.
numeric predictor matrix.
shape parameter for gamma prior distributions.
rate parameter for gamma prior distributions.
initial value for lambda parameter.
mu parameter for normal prior distributions.
standard deviation for normal prior distributions.
number of iterations for the Markov chain to run.
numeric burn-in length.
numeric thinning rate.
logical operator. TRUE to output plots.
logical operator. TRUE to print progress bar.
zero_poisson returns a list which includes the items
numeric vector; posterior distribution of lambda parameter
numeric matrix; posterior distributions of regression coefficients
numeric vector; posterior distribution of parameter 'p', the probability of a given zero observation belonging to the model's zero component
numeric vector; posterior log-likelihood
Fits a zero-inflated Poisson (ZIP) model.