MoEClust (version 1.4.1)

MoEClust-package: MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

Description

Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with gating and/or expert network covariates 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.

Usage

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 (plot.MoEClust) 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, MoE_compare, 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.

See Also

Further details and examples are given in the associated vignette document: vignette("MoEClust", package = "MoEClust")

Details

• Type: Package

• Package: MoEClust

• Version: 1.4.1

• Date: 2021-10-12 (this version), 2017-11-28 (original release)

• Licence: GPL (>=2)

References

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. <10.1007/s11634-019-00373-8>.

See Also

Useful links:

• https://cran.r-project.org/package=MoEClust

• Report bugs at https://github.com/Keefe-Murphy/MoEClust

Examples

# NOT RUN {
data(ais)

# Fit two sets of models
res1  <- MoE_clust(ais[,3:7], G=2, gating= ~ BMI, expert= ~ sex,
                   modelNames=c("VEE", "EVE", "VVE"), network.data=ais)
res2  <- MoE_clust(ais[,3:7], G=2, equalPro=TRUE, expert= ~ sex,
                   modelNames=c("VEE", "EVE", "VVE"), network.data=ais) 
        
# Compare the best model from each set of results
(comp <- MoE_compare(res1, res2, optimal.only=TRUE))

# Produce a plot for the optimal model                                                   
plot(comp$optimal, what="gpairs")

# Summarise its classification table, component parameters, and gating/expert networks
summary(comp$optimal, classification=TRUE, parameters=TRUE, networks=TRUE)

data(CO2data)
CO2   <- CO2data$CO2
GNP   <- CO2data$GNP

# Fit a range of models 
m1    <- MoE_clust(CO2, G=1:3)
m2    <- MoE_clust(CO2, G=2:3, gating= ~ GNP)
m3    <- MoE_clust(CO2, G=1:3, expert= ~ GNP)
m4    <- MoE_clust(CO2, G=2:3, gating= ~ GNP, expert= ~ GNP)
m5    <- MoE_clust(CO2, G=2:3, equalPro=TRUE)
m6    <- MoE_clust(CO2, G=2:3, expert= ~ GNP, equalPro=TRUE)

# Extract the model with highest BIC
(comp <- MoE_compare(m1, m2, m3, m4, m5, m6, criterion="bic"))
 
# See if a better model can be found using greedy forward stepwise selection
# Conduct a stepwise search on the same data
(mod1 <- MoE_stepwise(CO2, CO2data[,"GNP", drop=FALSE]))

# Conduct another stepwise search considering models with a noise component
(mod2 <- MoE_stepwise(CO2, CO2data[,"GNP", drop=FALSE], noise=TRUE))

# Compare all sets of results to choose the optimal model
(best <- MoE_compare(mod1, mod2, comp, pick=1)$optimal)
# }