Description Usage Arguments Value Author(s) Examples
covreg.mcmc is used to estimate the parameters in the covariance regression model providing Bayesian estimates.
1 2 | covreg.mcmc(fmean, fcov, data = NULL, R = 1, niter = 10000,
nthin = 10, nsave = niter/nthin, verb = T)
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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). |
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 |
Xiaoyue Niu and Peter Hoff
1 2 3 4 5 6 7 8 9 10 11 | ## 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)
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