joint.1z: Jointly modeling of multiple variables taking values from...
In zoib: Bayesian Inference for Beta Regression and Zero-or-One Inflated Beta Regression

Description Usage Arguments Value Author(s) See Also

Internal function to be called by function zoib. Jointly models multiple (0,1)-bounded variables with no inflation at 0 or 1 when there is a single random variable in the linear predictors of the link function.

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joint.1z(y, n, q, xmu.1, p.xmu, xsum.1, p.xsum, 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)

`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 fixed effects in linear predictor of the log(dispersion parameter of the beta piece).
`p.xsum`	Number of columns in xsum.1.
`rid`	A vector containing information on which linear predictor contains a random component.
`EUID`	Listing of the experimental units ID for each row of the data set
`nEU`	Number of experimental units
`prior1`	A vector containing information on the prior Prior choice for the regression coefficients in each of the 4 linear predictors of the 4 link functions.
`prior2`	A matrix containing 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 (a vector of dim = 4).
`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 information on the choice of link function 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