Performs MCMC sampling for a mixture model.
sampleSemisupervisedMVT(
X,
K,
B,
labels,
batch_vec,
fixed,
mu_proposal_window,
cov_proposal_window,
m_proposal_window,
S_proposal_window,
t_df_proposal_window,
R,
thin,
concentration,
m_scale,
rho,
theta,
initial_mu,
initial_cov,
initial_df,
initial_m,
initial_S,
mu_initialised,
cov_initialised,
df_initialised,
m_initialised,
S_initialised,
sample_m_scale
)Named list of the different quantities drawn by the sampler.
The data matrix to perform clustering upon (items to cluster in rows).
The number of components to model (upper limit on the number of clusters found).
The number of batches to model.
Vector item labels to initialise from.
Observed batch labels.
Binary vector of the items that are fixed in their initial label.
The standard deviation for the Gaussian proposal density of the cluster means.
The degrees of freedom for the Wishart proposal density of the cluster covariances.
The standard deviation for the Gaussian proposal density of the batch mean effects.
The rate for the Gamma proposal density of the batch scale.
The rate for the Gamma proposal density of the cluster degrees of freedom.
The number of iterations to run for.
thinning factor for samples recorded.
Vector of concentrations for mixture weights (recommended to be symmetric).
The scale hyperparameter for the batch shift prior distribution.
The shape of the prior distribution for the batch scale.
The scale of the prior distribution for the batch scale.
A P x K matrix of initial values for the class means.
A P x P x K cube of initial values for the class covariance matrices.
A K vector of initial values for the class degrees of freedom.
A P x B matrix of initial values for the batch shift effects.
A P x B matrix of initial values for the batch scales.
Bool indicating if the class means are initialised by the user. If ``false`` then initial values are drawn from the prior distribution.
Bool indicating if the class covariance matrices are initialised by the user. If ``false`` then initial values are drawn from the prior distribution.
Bool indicating if the class degrees of freedom are initialised by the user. If ``false`` then initial values are drawn from the prior distribution.
Bool indicating if the batch shift effects are initialised by the user. If ``false`` then initial values are drawn from the prior distribution.
Bool indicating if the batch scales are initialised by the user. If ``false`` then initial values are drawn from the prior distribution.
Bool indicating if the hyperparameter on the batch shift effect is sampled or given as fixed.