Extract the random effects variance covariance matrix Returns the posterior mean of the variance-covariance matrix/matrices of the random effects in a fitted JointAI object.

Extract the random effects variance covariance matrix Returns the posterior mean of the variance-covariance matrix/matrices of the random effects in a fitted JointAI object.

rd_vcov(object, outcome = NULL, start = NULL, end = NULL, thin = NULL,
  exclude_chains = NULL, mess = TRUE, warn = TRUE)

Arguments

object

object inheriting from class 'JointAI'

outcome

optional; vector of integers giving the indices of the outcomes for which the random effects variance-covariance matrix/matrices should be returned.

start

the first iteration of interest (see window.mcmc)

end

the last iteration of interest (see window.mcmc)

thin

thinning interval (integer; see window.mcmc). For example, thin = 1 (default) will keep the MCMC samples from all iterations; thin = 5 would only keep every 5th iteration.

exclude_chains

optional vector of the index numbers of chains that should be excluded

mess

logical; should messages be given? Default is TRUE.

warn

logical; should warnings be given? Default is TRUE.