Set the output structure for saving posterior samples of parameters.
initOutput(data, hyper, mc)
A list object including all the necessary data variables needed by the sampler.; output of the initData
function.
Hyper parameters for priors.
MCMC parameters.
A list of output parameters to be saved.
Vector of posterior samples for the concentration parameter in the Dirichlet process for the group-level latent classes.
Vector of posterior samples for the concentration parameter in the Dirichlet process for the individual-level latent classes. Currently, this is assumed to be the same within all group-level classes.
Matrix of posterior samples for the vector of probabilities for the group-level latent classes.
3D array of posterior samples for the matrix of probabilities for the group-level and individual-level latent class pairs.
Vector of posterior samples for the total number of impossible households sampled.
Matrix of posterior samples for the number of impossible households sampled, split by household size.
Vector of posterior samples for the number of occupied household-level latent classes.
Vector of posterior samples for the max number of occupied individual-level latent classes.
Vector of time taken to run each iteration.
3D array of posterior samples for the individual-level probabilities for each individual-level variable by each pair of group-level and individual-level latent classes.
A list of an array of posterior samples for the group-level probabilities for each group-level variable. Each array in the list is for each group-level variable.