Description Usage Arguments Value Author(s) See Also
MCMC algorithm for updating the second-component likelihood 
parameters in hurdle model regression using hurdle.
1 2  | update_beta(y, x, hurd, dist, like.part, beta.prior.mean, beta.prior.sd, beta,
  XB, beta.acc, beta.tune, g.x = F)
 | 
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.   | 
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.
Taylor Trippe <ttrippe@luc.edu> 
Earvin Balderama <ebalderama@luc.edu>
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