y: A univariate response variable taking value from [0, 1).
n: Number of rows in the data set.
xmu.1: Design matrix associated with the fixed effects in linear predictor of g(mean of the beta piece), where g is the link funciton.
p.xmu: Number of columns in xmu.1.
xsum.1: Design matrix associated with the fixed effects in linear predictor of the log(dispersion parameter of the beta piece).
p.xsum: Number of columns in xsum.1.
x0.1: Design matrix associated with the fixed effects in linear predictor of g(Pr(y=0)), where g is the link funciton.
p.x0: Number of columns in x0.1.
prior1: Internally created variable(a vector of dimension 4). Prior choice for the regression coefficients in the 4 linear predictors of the 4 link functions.
prec.int: The precision parmaeter of the prior distributions (diffuse normal) of the intercepts in the linear predictors.
prec.DN: The precision in the prior distributions of the regression coefficients in the linear predictors if the diffuse normal prior is chosen.
lambda.ARD: The scale parameter in the prior distributions of the regression coefficients in the linear predictors if the ARD prior is chosen.
lambda.L1: The scale parameter in the prior distributions of the regression coefficients in the linear predictors if the L1-like prior is chosen.
lambda.L2: The scale parameter in the prior distributions of the regression coefficients in the linear predictors if the L2-like prior is chosen.
link: Internally created variable containing the information on the choice of link functions.
n.chain: Number of chains for the MCMC sampling.
inits: initial parameter for model parameters.
seed: seeds for results reproducibility
Returns
Internal function. Returned values are used internally.