# batchmix

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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) for details of the model.

## Advice on using the package

The main functions a user should be aware of are
`runMCMCChains`

, `plotLikelihoods`

,
`plotAcceptanceRates`

, `continueChains`

and
`processChains`

.

Parameters are sampled using Metropolis-Hastings so checking that the
acceptance rate is important. We recommend aiming for acceptance rates
between 0.1 and 0.5 for the class and batch means and batch scales
(\(\mu_k\), \(m_b\) and \(S_b\) respectively). In our testing, an
acceptance rate of at least 0.4 for the class covariance matrices tended
to suggest the sampler is exploring well, but smaller values were
frequently associated with poor behaviour. The degrees of freedom tend
to have very high acceptance rates in our testing regardless of the
sampling window.

We recommend running a small number of chains for a small number of
iterations to assess the acceptance rates before committing the
computational resourcces to run a full analysis.

For an example of a workflow please see the short vignette.