Fits MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>, i.e. fits finite Gaussian mixture of experts models with gating and/or expert network covariates supplied via formula interfaces using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated.
The most important function in the MoEClust package
is: MoE_clust, for fitting the model via EM/CEM with gating
and/or expert network covariates, supplied via formula interfaces.
MoE_compare is provided for conducting model selection
between different results from MoE_clust using different
covariate combinations &/or initialisation strategies, etc.
MoE_stepwise is provided for conducting a greedy forward
stepwise search to identify the optimal model in terms of the number of
components, GPCM covariance type, and the subsets of gating/expert
network covariates.
MoE_control allows supplying additional arguments to
MoE_clust and MoE_stepwise which govern, among
other things, controls on the inclusion of an additional noise component
and controls on the initialisation of the allocations for the EM/CEM
algorithm.
A dedicated plotting function exists for visualising the results
using generalised pairs plots, for examining the gating network, and/or
log-likelihood, and/or clustering uncertainties, and/or graphing model
selection criteria values. The generalised pairs plots
(MoE_gpairs) visualise all pairwise relationships between
clustered response variables and associated continuous, categorical,
and/or ordinal covariates in the gating &/or expert networks,
coloured according to the MAP classification, and also give the marginal
distributions of each variable (incl. the covariates) along the
diagonal.
An as.Mclust method is provided to coerce the output of
class "MoEClust" from MoE_clust to the
"Mclust" class, to facilitate use of plotting and other
functions for the "Mclust" class within the
mclust package. As per mclust,
MoEClust also facilitates modelling with an additional
noise component (with or without the mixing proportion for the noise
component depending on covariates). Finally, a predict
method is provided for predicting the fitted response and probability of
cluster membership (and by extension the MAP classification) for new
data, in the form of new covariates and new response data, or new
covariates only.
Other functions also exist, e.g. MoE_crit,
MoE_dens, MoE_estep, and aitken,
which are all used within MoE_clust but are nonetheless
made available for standalone use. The package also contains two data
sets: ais and CO2data.
You can install the latest stable official release of the
MoEClust package from CRAN:
install.packages("MoEClust")or the development version from GitHub:
# If required install devtools:
# install.packages('devtools')
devtools::install_github('Keefe-Murphy/MoEClust')In either case, you can then explore the package with:
library(MoEClust)
help(MoE_clust) # Help on the main modelling functionFor a more thorough intro, the vignette document is available as follows:
vignette("MoEClust", package="MoEClust")However, if the package is installed from GitHub the vignette is not automatically created. It can be accessed when installing from GitHub with the code:
devtools::install_github('Keefe-Murphy/MoEClust', build_vignettes = TRUE)Alternatively, the vignette is available on the package’s CRAN page.
Murphy, K. and Murphy, T. B. (2020). Gaussian parsimonious clustering models with covariates and a noise component. Advances in Data Analysis and Classification, 14(2): 293–325. <doi:10.1007/s11634-019-00373-8>.