mdir (version 0.9.0)

processMCMCChain: Process MCMC chain

Description

Applies a burn in to and finds a point estimate for the output of ``batchSemiSupervisedMixtureModel``.

Usage

processMCMCChain(
  mcmc_output,
  burn,
  point_estimate_method = "median",
  construct_psm = FALSE
)

Value

A named list similar to the output of ``batchSemiSupervisedMixtureModel`` with some additional entries: * ``allocation_probability``: $(N x K)$ matrix. The point estimate of the allocation probabilities for each data point to each class.

* ``prob``: $N$ vector. The point estimate of the probability of being allocated to the class with the highest probability.

* ``pred``: $N$ vector. The predicted class for each sample.

Arguments

mcmc_output

Output from ``batchSemiSupervisedMixtureModel``

burn

The number of MCMC samples to drop as part of a burn in.

point_estimate_method

Summary statistic used to define the point estimate. Must be ``'mean'`` or ``'median'``. ``'median'`` is the default.

construct_psm

Logical indicating if PSMs be constructed in the unsupervised views. Defaults to FALSE. If TRUE the PSM is constructed and this is used to infer the point estimate rather than the sampled partitions.

Examples

N <- 100
X <- matrix(c(rnorm(N, 0, 1), rnorm(N, 3, 1)), ncol = 2, byrow = TRUE)
Y <- matrix(c(rnorm(N, 0, 1), rnorm(N, 3, 1)), ncol = 2, byrow = TRUE)

truth <- c(rep(1, N / 2), rep(2, N / 2))
data_modelled <- list(X, Y)

V <- length(data_modelled)

# This R is much too low for real applications
R <- 100
thin <- 5
burn <- 10

K_max <- 10
K <- rep(K_max, V)
types <- rep("G", V)

mcmc_out <- callMDI(data_modelled, R, thin, types, K = K)
processMCMCChain(mcmc_out, burn)