as.Mclust: Convert MoEClust objects to the Mclust class

Description

Converts an object of class "MoEClust" generated by MoE_clust and converts it to an object of class "Mclust" as generated by fitting Mclust, to facilitate use of plotting and other functions for the "Mclust" class within the mclust package. Some caution is advised when converting models with gating &/or expert covariates (see Note below) and users are always encouraged to use the dedicated plot.MoEClust function for objects of the "MoEClust" class instead.

Usage

# S3 method for MoEClust
as.Mclust(x,
         expert.covar = TRUE,
         signif = 0L,
         ...)

Value

An object of class "Mclust". See methods(class="Mclust") for a (non-exhaustive) list of functions which can be applied to this class.

Arguments

x: An object of class "MoEClust" generated by MoE_clust or an object of class "MoECompare" generated by MoE_compare. Models with a noise component are facilitated here too.
expert.covar: Logical (defaults to TRUE) governing whether the extra variability in the component means is added to the MVN ellipses corresponding to the component covariance matrices in the presence of expert network covariates. See the function expert_covar.
signif: Significance level for outlier removal. Must be a single number in the interval [0, 1). Corresponds to the percentage of data to be considered extreme and therefore removed (half of signif at each endpoint, on a column-wise basis). The default, 0, corresponds to no outlier removal. Only invoke this argument as an aid to visualisation via plot.Mclust.
...: Further arguments to be passed to other methods.

Author

Keefe Murphy - <keefe.murphy@mu.ie>

Details

Mixing proportions are averaged over observations in components in the presence of gating network covariates during the coercion. For models with expert network covariates, the means are given by the posterior means of the fitted values of the expert network.

In the presence of expert network covariates, the component-specific covariance matrices are (by default, via the argument expert.covar) modified for plotting purposes via the function expert_covar, in order to account for the extra variability of the means, usually resulting in bigger shapes & sizes for the MVN ellipses.

The signif argument is intended only to aid visualisation via plot.Mclust, as plots therein can be sensitive to outliers, particularly with regard to axis limits. However, users are always encouraged to use the dedicated plot.MoEClust function for objects of the "MoEClust" class instead (see Note below).

References

Fraley, C. and Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458): 611-631.

Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016). mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1): 289-317.

Examples

Run this code

library(mclust)

# Fit a gating network mixture of experts model to the ais data
data(ais)
mod   <- MoE_clust(ais[,3:7], G=3, gating= ~ BMI + sex, modelNames="EEE", network.data=ais)

# Convert to the "Mclust" class and examine the classification
mod2  <- as.Mclust(mod)
plot(mod2, what="classification")

# Examine the uncertainty
plot(mod2, what="uncertainty")

# Return the optimal number of clusters according to entropy
combi <- mclust::clustCombi(object=mod2)
optim <- mclust::clustCombiOptim(object=combi)
table(mod2$classification, ais$sex)
table(optim$cluster.combi, ais$sex)

# Compare plot.MoEClust and plot.Mclust for univariate mixtures
data(CO2data)
res <- MoE_clust(CO2data$CO2, G=2, expert = ~ GNP, modelNames="V", network.data=CO2data)
plot(as.Mclust(res), what="classification")
plot(as.Mclust(res), what="density")
plot(as.Mclust(res), what="uncertainty") 

# Proper version of what="density" plot:
MoE_gpairs(res, show.map=FALSE, cov.ind=0, show.dens=TRUE)

# Equivalent what="uncertainty" plot:
MoE_Uncertainty(res)

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