MoE_Uncertainty: Plot Clustering Uncertainties

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

Plot Clustering Uncertainties

Description

Plots the clustering uncertainty for every observation from a fitted "MoEClust" model, including models with a noise component.

Usage

MoE_Uncertainty(res,
                type = c("barplot", "profile"),
                truth = NULL,
                decreasing = FALSE,
                ...)

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.
type	The type of plot to be produced (defaults to "barplot"). The "profile" option instead displays uncertainties in increasing/decreasing order of magnitude (see decreasing).
truth	An optional argument giving the true classification of the data. When truth is supplied and type="barplot", misclassified observations are highlighted in a different colour, otherwise observations with uncertainty greater than 1/res$G are given in a different colour. When truth is supplied and type="profile", the uncertainty of misclassified observations is marked by vertical lines on the plot.
decreasing	Logical indicating whether uncertainties should be ordered in decreasing order (defaults to FALSE). Only relevant when type="profile".
...	Catches unused arguments.

Details

The y-axis of this plot runs from 0 to 1 - 1/res$G, with a horizontal line also drawn at 1/res$G. When type="barplot", uncertainties greater than this value are given a different colour when truth is not supplied, otherwise misclassified observations are given a different colour. Note, however, that G^{(0)} = res$G + 1 is used in place of res$G for models with a noise component.

Value

A plot showing the clustering uncertainty of each observation (sorted in increasing/decreasing order when type="profile"). The (unsorted) vector of uncertainties can also be returned invisibly. When truth is supplied, the indices of the misclassified observations are also invisibly returned.

Note

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

An alternative means of visualising clustering uncertainties (at least for multivariate data) is provided by the functions MoE_gpairs and plot.MoEClust, specifically when their argument response.type is given as "uncertainty".

Author(s)

Keefe Murphy - <[e-mail]>

See Also

MoE_clust, MoE_gpairs, plot.MoEClust

Examples

data(ais)
res <- MoE_clust(ais[,3:7], gating= ~ sex, G=3, modelNames="EEE", network.data=ais)

# Produce an uncertainty barplot
MoE_Uncertainty(res)

# Produce an uncertainty profile plot
MoE_Uncertainty(res, type="profile")

# Let's assume the true clusters correspond to sex
(ub <- MoE_Uncertainty(res, truth=ais$sex))
(up <- MoE_Uncertainty(res, type="profile", truth=ais$sex))

MoEClust documentation built on May 29, 2024, 6:44 a.m.