psmMergeSplit_base {sams}

Base Functionality for the psmMergeSplit Function

Description

Merge-split proposals for conjugate "Chinese Restaurant Process" (CRP) mixture models using sequentially-allocated elements. Allocation is performed with weights derived from a previously-calculated pairwise similarity matrix.

Usage

psmMergeSplit_base(
  partition,
  psm,
  logPosteriorPredictiveDensity = function(i, subset) 0,
  mass = 1,
  discount = 0,
  nUpdates = 1L,
  selectionWeights = NULL
)

Arguments

partition

A numeric vector of cluster labels representing the current partition.

psm

A matrix of previously-calculated pairwise similarity probabilities for each pair of data indices.

logPosteriorPredictiveDensity

A function taking an index i (as a numeric vector of length one) and a subset of integers subset, and returning the natural logarithm of p( y_i | y_subset ), i.e., that item's contribution to the log integrated likelihood given a subset of the other items. The default value "turns off" the likelihood, resulting in prior simulation (rather than posterior simulation).

mass

A specification of the mass (concentration) parameter in the CRP prior. Must be greater than the -discount argument.

discount

A numeric value on the interval [0,1) corresponding to the discount parameter in the two-parameter CRP prior.

nUpdates

An integer giving the number of merge-split proposals before returning. This has the effect of thinning the Markov chain.

selectionWeights

A matrix or data frame whose first two columns are the unique pairs of data indices, along with a column of weights representing how likely each pair is to be selected at the beginning of each merge-split update.

Value

partition

A numeric vector giving the updated partition encoded using cluster labels.

accept

The acceptance rate of the Metropolis-Hastings proposals, i.e. the number of accepted proposals divided by nUpdates.

See Also

psmMergeSplit