MCMC algorithm for updating the likelihood probabilities in
hurdle model regression using hurdle.
update_probs(y, x, hurd, p, q, beta.prior.mean, beta.prior.sd, pZ, pT, pE, beta,
XB2, XB3, beta.acc, beta.tune)numeric response vector.
optional numeric predictor matrix.
numeric threshold for 'extreme' observations of two-hurdle models.
numeric vector of current 'p' probability parameter values for zero-value observations.
numeric vector of current 'q' probability parameter values for 'extreme' observations.
mu parameter for normal prior distributions.
standard deviation for normal prior distributions.
numeric vector of current 'zero probability' likelihood values.
numeric vector of current 'typical probability' likelihood values.
numeric vector of current 'extreme probability' likelihood values.
numeric matrix of current regression coefficient parameter values.
\(x*beta[,2]\) product matrix.
\(x*beta[,3]\) product matrix.
numeric matrix of current MCMC acceptance rates for regression coefficient parameters.
numeric matrix of current MCMC tuning values for regression coefficient estimation.
A list of MCMC-updated regression coefficients for the estimation of the parameters 'p' (the probability of a zero-value observation) and 'q' (the probability of an 'extreme' observation) as well as each coefficient's MCMC acceptance ratio.