MoEClust (version 1.4.1)

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).

Usage

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

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.

Value

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

Details

Of course, the user is always encouraged to use the dedicated plot function for objects of the "MoEClust" class instead, but calling plot after converting via as.Mclust can be particularly useful for univariate mixtures.

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.

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.

See Also

Mclust, plot.Mclust, MoE_clust, plot.MoEClust, expert_covar, MoE_control

Examples

# NOT RUN {
# library(mclust)

# Fit a gating network mixture of experts model to the ais data
# data(ais)
# mod   <- MoE_clust(ais[,3:7], G=1:9, gating= ~ BMI + sex, 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)

# While we could have just used plot.MoEClust above,
# plot.Mclust is especially useful for univariate data
# data(CO2data)
# res <- MoE_clust(CO2data$CO2, G=3, equalPro=TRUE, expert = ~ GNP, network.data=CO2data)
# plot(as.Mclust(res))
# }