Usage
fixed0(y, n, xmu.1, p.xmu, xsum.1, p.xsum, x0.1, p.x0, prior1, prec.int, prec.DN,
lambda.L1, lambda.L2, lambda.ARD, link, n.chain)
Arguments
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.