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