MoE_plotCrit: Model Selection Criteria Plot for MoEClust Mixture Models

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

Model Selection Criteria Plot for MoEClust Mixture Models

Description

Plots the BIC, ICL, AIC, or log-likelihood values of a fitted MoEClust object.

Usage

MoE_plotCrit(res,
             criterion = c("bic", "icl", "aic", "loglik", "df", "iters"),
             ...)

Arguments

res	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.
criterion	The criterion to be plotted. Defaults to "bic". Recall that MoE_control only allows "bic", "icl", and "aic" to be used as model selection criteria within MoE_clust. The same applies to MoE_control. Uppercase crit will be coerced to lowercase.
...	Catches other arguments, or additional arguments to be passed to plot.mclustBIC (or equivalent functions for the other criterion arguments). In particular, the argument legendArgs to plot.mclustBIC can be passed.

Value

A plot of the values of the chosen criterion. The values themselves can also be returned invisibly.

Note

plot.MoEClust is a wrapper to MoE_plotCrit which accepts the default arguments, and also produces other types of plots.

Author(s)

Keefe Murphy - <[e-mail]>

See Also

MoE_clust, MoE_control, plot.MoEClust, plot.mclustBIC

Examples

# data(ais)
# res   <- MoE_clust(ais[,3:7], expert= ~ sex, network.data=ais)
# (crit <- MoE_plotCrit(res))

# Plots can also be produced directly
# plot(res$ICL)

MoEClust documentation built on April 3, 2025, 11:07 p.m.