zero_poisson {hurdlr}

Zero-Inflated Poisson Regression Model

Description

zero_poisson is used to fit zero-inflated poisson regression models to count data via Bayesian inference.

Usage

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)

Arguments

y

numeric response vector.

x

numeric predictor matrix.

a

shape parameter for gamma prior distributions.

b

rate parameter for gamma prior distributions.

lam.start

initial value for lambda parameter.

beta.prior.mean

mu parameter for normal prior distributions.

beta.prior.sd

standard deviation for normal prior distributions.

iters

number of iterations for the Markov chain to run.

burn

numeric burn-in length.

nthin

numeric thinning rate.

plots

logical operator. TRUE to output plots.

progress.bar

logical operator. TRUE to print progress bar.

Details

Fits a zero-inflated Poisson (ZIP) model.

Value

zero_poisson returns a list which includes the items

lam

numeric vector; posterior distribution of lambda parameter

beta

numeric matrix; posterior distributions of regression coefficients

p

numeric vector; posterior distribution of parameter 'p', the probability of a given zero observation belonging to the model's zero component

ll

numeric vector; posterior log-likelihood

Author(s)

Taylor Trippe <[e-mail]>
Earvin Balderama <[e-mail]>