R: MCMC Second-Component Parameter Update Function for Hurdle...

update_beta {hurdlr}

R Documentation

MCMC Second-Component Parameter Update Function for Hurdle Model Count Data Regression

Description

MCMC algorithm for updating the second-component likelihood parameters in hurdle model regression using hurdle.

Usage

update_beta(y, x, hurd, dist, like.part, beta.prior.mean, beta.prior.sd, beta,
  XB, beta.acc, beta.tune, g.x = F)

Arguments

`y`	numeric response vector of observations within the bounds of the second component of the likelihood function, `y[0 < y \& y < hurd]`
`x`	optional numeric predictor matrix for response observations within the bounds of the second component of the likelihood function, `y[0 < y \& y < hurd]`.
`hurd`	numeric threshold for 'extreme' observations of two-hurdle models.
`dist`	character specification of response distribution for the third component of the likelihood function.
`like.part`	numeric vector of the current third-component likelihood values.
`beta.prior.mean`	mu parameter for normal prior distributions.
`beta.prior.sd`	standard deviation for normal prior distributions.
`beta`	numeric matrix of current regression coefficient parameter values.
`XB`	`x*beta[,1]` product matrix for response observations within the bounds of the second component of the likelihood function, `y[0 < y \& y < hurd]`.
`beta.acc`	numeric matrix of current MCMC acceptance rates for regression coefficient parameters.
`beta.tune`	numeric matrix of current MCMC tuning values for regression coefficient estimation.
`g.x`	logical operator. `TRUE` if operating within the third component of the likelihood function (the likelihood of 'extreme' observations).

Value

A list of MCMC-updated regression coefficients for the estimation of the second-component likelihood parameter as well as each coefficient's MCMC acceptance ratio.

Author(s)