runMCMCChains {batchmix}

Run MCMC Chains

Description

Run multiple chains of the batch mixture model of the same type.

Usage

runMCMCChains(
  X,
  n_chains,
  R,
  thin,
  batch_vec,
  type,
  K_max = NULL,
  initial_labels = NULL,
  fixed = NULL,
  alpha = 1,
  mu_proposal_window = 0.5^2,
  cov_proposal_window = 0.002,
  m_proposal_window = 0.3^2,
  S_proposal_window = 0.01,
  t_df_proposal_window = 0.015,
  m_scale = 0.01,
  rho = 3,
  theta = 1,
  initial_class_means = NULL,
  initial_class_covariance = NULL,
  initial_batch_shift = NULL,
  initial_batch_scale = NULL,
  initial_class_df = NULL,
  verbose = TRUE
)

Arguments

X

Data to cluster as a matrix with the items to cluster held in rows.

n_chains

Integer. Number of MCMC chains to run.

R

The number of iterations in the sampler.

thin

The factor by which the samples generated are thinned, e.g. if “thin=50“ only every 50th sample is kept.

batch_vec

Labels identifying which batch each item being clustered is from.

type

Character indicating density type to use. One of 'MVN' (multivariate normal distribution) or 'MVT' (multivariate t distribution). “weights“ which is a matrix with K x B columns. The columns are ordered by batch, i.e. the first K columns contain the class weights in the first batch, the second K are the class weights in the second batch, etc. If generic weights are used then this matrix has K columns, one for each component weight.

K_max

The number of components to include (the upper bound on the number of clusters in each sample). Defaults to the number of unique labels in “initial_labels“.

initial_labels

Initial clustering, if none given defaults to a random draw.

fixed

Which items are fixed in their initial label. If not given, defaults to a vector of 0 meaning the model is run unsupervised.

alpha

The concentration parameter for the stick-breaking prior and the weights in the model.

mu_proposal_window

The proposal window for the cluster mean proposal kernel. The proposal density is a Gaussian distribution, the window is the variance.

cov_proposal_window

The proposal window for the cluster covariance proposal kernel. The proposal density is a Wishart distribution, this argument is the reciprocal of the degree of freedom.

m_proposal_window

The proposal window for the batch mean proposal kernel. The proposal density is a Gaussian distribution, the window is the variance.

S_proposal_window

The proposal window for the batch standard deviation proposal kernel. The proposal density is a Gamma distribution, this argument is the reciprocal of the rate.

t_df_proposal_window

The proposal window for the degrees of freedom for the multivariate t distribution (not used if type is not 'MVT'). The proposal density is a Gamma distribution, this argument is the reciprocal of the rate.

m_scale

The scale hyperparameter for the batch shift prior distribution. This defines the scale of the batch effect upon the mean and should be in (0, 1].

rho

The shape of the prior distribution for the batch scale.

theta

The scale of the prior distribution for the batch scale.

initial_class_means

A $P x K$ matrix of initial values for the class means. Defaults to draws from the prior distribution.

initial_class_covariance

A $P x P x K$ array of initial values for the class covariance matrices. Defaults to draws from the prior distribution.

initial_batch_shift

A $P x B$ matrix of initial values for the batch shift effect Defaults to draws from the prior distribution.

initial_batch_scale

A $P x B$ matrix of initial values for the batch scales Defaults to draws from the prior distribution.

initial_class_df

A $K$ vector of initial values for the class degrees of freedom. Defaults to draws from the prior distribution.

verbose

Logiccal indicating if warning about proposal windows should be printed.

Value

A list of named lists. Each entry is the output of “runBatchMix“.

Examples

# Data in a matrix format
X <- matrix(c(rnorm(100, 0, 1), rnorm(100, 3, 1)), ncol = 2, byrow = TRUE)

# Initial labelling
labels <- c(
  rep(1, 10),
  sample(c(1, 2), size = 40, replace = TRUE),
  rep(2, 10),
  sample(c(1, 2), size = 40, replace = TRUE)
)

fixed <- c(rep(1, 10), rep(0, 40), rep(1, 10), rep(0, 40))

# Batch
batch_vec <- sample(seq(1, 5), replace = TRUE, size = 100)

# Sampling parameters
R <- 1000
thin <- 50
n_chains <- 4

# MCMC samples
samples <- runMCMCChains(X, n_chains, R, thin, batch_vec, "MVN",
  initial_labels = labels,
  fixed = fixed
)