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

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Description

Internal function to be called by function zoib; Separately 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

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sep.2z0(y, n, xmu.1, p.xmu, xsum.1, p.xsum, x0.1, p.x0, 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.

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.

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.

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 element that contains the number of levels of each random varaibles.

rid

A vector containing 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 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 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 paratmer of the prior distributions (diffuse normal) of the intercepts in the linear predictors.

prec.DN

The precision paratmer of 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.

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

Value

Internal function. Returned values are used internally

Author(s)

Fang Liu (fang.liu.131@nd.edu)

See Also

See Also as zoib

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