joint.2z0: Jointly modelling of multiple variables taking values from...

In zoib: Bayesian Inference for Beta Regression and Zero-or-One Inflated Beta Regression

Jointly modelling of multiple variables taking values from [0,1) when there are multiple random variables in the linear predictors of the link functions

Description

Internal function to be called by function zoib. Jointly models multiple [0,1)-bounded variables with inflation at 0 when there are multiple random variables in the linear predictors of the link functions

Usage

joint.2z0(y, n, q, xmu.1, p.xmu, xsum.1, p.xsum, x0.1, p.x0, inflate0, zdummy, 
qz, nz0, m, rid, EUID, nEU, prior1, prior2, prior.beta, prior.Sigma, prec.int, 
prec.DN, lambda.L1, lambda.L2, lambda.ARD, scale.unif, scale.halft, link, n.chain,
inits, seed)

Arguments

y	>=2 response variables taking value from [0, 1).
n	Number of rows in the data set.
q	Number of response variables.
xmu.1	Design matrix associated with the fixed effects in the linear predictor of g(mean of the beta piece), where g() is a link function.
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 the linear predictor of g(Pr(y=0)), where g() is a link function.
p.x0	Number of columns in x0.1.
inflate0	A vector containing information on which response variables have inflation at 0.
zdummy	Design matrix associated with the random effects.
qz	Number of columns in zdummy.
nz0	Number of original random variables (before dummy coding).
m	A vector with nz0 elements that contains the number of levels of each random varaibles.
rid	A vector containing the information on which linear predictors have a random component.
EUID	Listing of experimental unit ID for each row of the data set
nEU	Number of experimental units
prior1	A vector containing the information on the prior choice for the regression coefficients in each of the 4 linear predictors of the 4 link functions.
prior2	A matrix containing the information on the prior choice for the covariance structure of the random variables.
prior.beta	Prior choice for the regression coefficients in each of the 4 link functions.
prior.Sigma	Prior choice for the covariance structure of the random variables.
prec.int	The precision parameter of the prior distributions (diffuse normal) of the intercepts in the linear predictors.
prec.DN	The precision parameter of the prior distributions of the regression coefficients in the linear predictors if 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.
scale.unif	The upper bound of the uniform distribution for the standard deviation of each random variable
scale.halft	The scale parameter of the half-Cauchy distribution for the standard deviation of each random variable
link	A vector containing the information on the choice of link functions for the mean of the beta piece.
n.chain	Number of chains for the MCMC sampling.
inits	initial parameter for model parameters.
seed	seeds for results reproducibility

Value

Internal function. Returned values are used internally

Author(s)

Fang Liu ([e-mail])

See Also

See Also as zoib

zoib documentation built on May 31, 2023, 7:49 p.m.