hurdle_control {hurdlr}R Documentation

Control Parameters for Hurdle Model Count Data Regression

Description

Various parameters for fitting control of hurdle model regression using hurdle.

Usage

hurdle_control(a = 1, b = 1, size = 1, beta.prior.mean = 0,
  beta.prior.sd = 1000, beta.tune = 1, pars.tune = 0.2, lam.start = 1,
  mu.start = 1, sigma.start = 1, xi.start = 1)

Arguments

a

shape parameter for gamma prior distributions.

b

rate parameter for gamma prior distributions.

size

size parameter for negative binomial likelihood distributions.

beta.prior.mean

mu parameter for normal prior distributions.

beta.prior.sd

standard deviation for normal prior distributions.

beta.tune

Markov-chain tuning for regression coefficient estimation.

pars.tune

Markov chain tuning for parameter estimation of 'extreme' observations distribution.

lam.start

initial value for the poisson likelihood lambda parameter.

mu.start

initial value for the negative binomial or log normal likelihood mu parameter.

sigma.start

initial value for the generalized pareto likelihood sigma parameter.

xi.start

initial value for the generalized pareto likelihood xi parameter.

Value

A list of all input values.

Author(s)

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

See Also

hurdle


[Package hurdlr version 0.1 Index]