covreg.mcmc {covreg} R Documentation

## Bayesian estimation of the covariance regressioin model

### Description

covreg.mcmc is used to estimate the parameters in the covariance regression model providing Bayesian estimates.

### Usage

```covreg.mcmc(fmean, fcov, data = NULL, R = 1, niter = 10000,
nthin = 10, nsave = niter/nthin, verb = T)
```

### Arguments

 `fmean` an object of class "formula", model for the mean regression. `fcov` an object of class "formula", model for the covariance regression. Can be different from the mean model. `data` data frame containing variables in the model. `R` a positive integer, rank of the model. `niter` number of MCMC iterations. `nthin` number of thinning. `nsave` number of output iterations, calualted as niter/nthin. `verb` print progress of MCMC(TRUE/FALSE).

### Value

 `B1.psamp` an array containing the MCMC samples of the mean regression coefficients `B2.psamp` an array containing the MCMC samples of the covariance regression coefficients `A.psamp` an array containing the MCMC samples of the baseline covariance matrix `matrix.mean` the design matrix of the mean regression `matrix.cov` the design matrix of the covariance regression

### Author(s)

Xiaoyue Niu and Peter Hoff

### Examples

```## load FEV data ##
data(fev)
## specify mean and cov models ##
library(splines)
fmean=as.formula(cbind(fev,height)~bs(age,knots=11))
fcov=as.formula(cbind(fev,height)~sqrt(age)+age)
## fit model ##
fit<-covreg.mcmc(fmean,fcov,data=fev,R=2,niter=100,nthin=1)
## summarize MCMC samples ##
M.psamp=m.psamp(fit)
S.psamp=cov.psamp(fit)
```

[Package covreg version 1.0 Index]