hurdle: Hurdle Model Count Data Regression
In hurdlr: Zero-Inflated and Hurdle Modelling Using Bayesian Inference

Description Usage Arguments Details Value Author(s) Examples

hurdle is used to fit single or double-hurdle regression models to count data via Bayesian inference.

hurdle(y, x = NULL, hurd = Inf, dist = c("poisson", "nb", "lognormal"),
  dist.2 = c("gpd", "poisson", "lognormal", "nb"),
  control = hurdle_control(), iters = 1000, burn = 500, nthin = 1,
  plots = FALSE, progress.bar = TRUE)

`y`	numeric response vector.
`x`	numeric predictor matrix.
`hurd`	numeric threshold for 'extreme' observations of two-hurdle models. `Inf` for one-hurdle models.
`dist`	character specification of response distribution.
`dist.2`	character specification of response distribution for 'extreme' observations of two-hurdle models.
`control`	list of parameters for controlling the fitting process, specified by `hurdle_control`.
`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.

Setting dist and dist.2 to be the same distribution creates a single dist-hurdle model, not a double-hurdle model. However, this is being considered in future package updates.

hurdle returns a list which includes the items

pD: measure of model dimensionality p_D where p_D = \bar{D} - D(\bar{θ}) is the "mean posterior deviance - deviance of posterior means"
DIC: Deviance Information Criterion where DIC = \bar{D} - p_D
PPO: Posterior Predictive Ordinate (PPO) measure of fit
CPO: Conditional Predictive Ordinate (CPO) measure of fit
pars.means: posterior mean(s) of third-component parameter(s) if hurd != Inf
ll.means: posterior means of the log-likelihood distributions of all model components
beta.means: posterior means regression coefficients
dev: posterior deviation where D = -2LogL
beta: posterior distributions of regression coefficients
pars: posterior distribution(s) of third-component parameter(s) if hurd != Inf

Taylor Trippe <ttrippe@luc.edu>
Earvin Balderama <ebalderama@luc.edu>

#Generate some data:
p=0.5; q=0.25; lam=3;
mu=10; sigma=7; xi=0.75;
n=200

set.seed(2016)
y <- rbinom(n,1,p)
nz <- sum(1-y)
extremes <- rbinom(sum(y),1,q)
ne <- sum(extremes)
nt <- n-nz-ne
yt <- sample(mu-1,nt,replace=TRUE,prob=dpois(1:(mu-1),3)/(ppois(mu-1,lam)-ppois(0,lam)))
yz <- round(rgpd(nz,mu,sigma,xi))
y[y==1] <- c(yt,yz)
g <- hurdle(y)