GLMM_MCMC(y, dist="gaussian", id, x, z, random.intercept,
prior.beta, init.beta,
scale.b, prior.b, init.b,
prior.eps, init.eps,
nMCMC=c(burn=10, keep=10, thin=1, info=10),
store=c(b=FALSE), keep.chains=TRUE)## S3 method for class 'GLMM_MCMC':
print(x, \dots)
y is vector then
there is only one response in the model. If y is matrix or data frame then
each column gives values of one response. Missing values are allowed.If
random.intercept.prior.b can have the components listed belowlmer functions)
and does not havlmer functions)
and does not have to be given by the uprior.eps can
have the components listed below. For all components, a sensible
value leading to weakly informative prior distribution can init.eps can have the components listed below. For all
components, a sensible value can be determined by the fuFALSE, only summary statistics
are returned in the resulting object. This might be useful in the
model searching step to save some memory.print method.GLMM_MCMC. It can have the following
components (some of them may be missing according to the context
of the model):nMCMC.dist argument.prior.beta.prior.b.prior.eps.init.beta.init.b.init.eps.init.beta to restart MCMC.b, K, w, mu, Sigma, Li, Q,
gammaInv, r. It can be used as argument
init.b to restart MCMC.sigma, gammaInv. It can be used as argument
init.eps to restart MCMC.scale.b.data.frame with columns labeled
fixed and random holding posterior means for fixed
effect part of the linear predictor and the random effect part of
the linear predictor. In each column, there are first all values for
the first response, then all values for the second response etc.data.frame with columns labeled
b1, ..., bq, LogL, Logpb with
posterior means of random effects for each cluster and posterior
means of $\log(L)$ (log-likelihood given random effects)
and $\log\bigl{p(\boldsymbol{b})\bigr}$ for each cluster.NMixMCMC.