batchmix-package: Bayesian Mixture Modelling for Joint Model-Based...

In batchmix: Semi-Supervised Bayesian Mixture Models Incorporating Batch Correction

Bayesian Mixture Modelling for Joint Model-Based Clustering/Classification and Batch Correction

Description

Semi-supervised and unsupervised Bayesian mixture models that simultaneously infer the cluster/class structure and a batch correction. Densities available are the multivariate normal and the multivariate t. The model sampler is implemented in C++. This package is aimed at analysis of low-dimensional data generated across several batches. See (Coleman et al. (2022))[https://doi.org/10.1101/2022.01.14.476352] for details of the model.

Author(s)

Stephen Coleman <[e-mail]>, Paul D.W. Kirk, Chris Wallace

See Also

Useful links:

• https://github.com/stcolema/batchmix

• Report bugs at https://github.com/stcolema/batchmix/issues

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)
)

# Which labels are observed
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

# Classification
samples <- runBatchMix(X,
  R,
  thin,
  batch_vec,
  "MVN",
  initial_labels = labels,
  fixed = fixed,
)

# Clustering
samples <- runBatchMix(X, R, thin, batch_vec, "MVT")

batchmix documentation built on May 29, 2024, 2:14 a.m.