plot.MoEClust: Plot MoEClust Results

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

Plot MoEClust Results

Description

Plot results for fitted MoE_clust mixture models with gating &/or expert network covariates: generalised pairs plots, model selection criteria, the log-likelihood vs. the EM iterations, and the gating network are all currently visualisable.

Usage

## S3 method for class 'MoEClust'
plot(x,
     what = c("gpairs", "gating", "criterion", "loglik", "similarity", "uncertainty"),
     ...)

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.
what	The type of graph requested: gpairs A generalised pairs plot. To further customise this plot, arguments to MoE_gpairs can be supplied. gating The gating network. To further customise this plot, arguments to MoE_plotGate and matplot can be supplied. criterion The model selection criteria. To further customise this plot, arguments to MoE_plotCrit and plot.mclustBIC can be supplied. loglik The log-likelihood vs. the iterations of the EM algorithm. To further customise this plot, arguments to MoE_plotLogLik and plot can be supplied. similarity The similarity matrix constructed from x$z at convergence, in the form of a heatmap. To further customise this plot, arguments to MoE_Similarity can be supplied. uncertainty The clustering uncertainty for every observation. To further customise this plot, arguments to MoE_Uncertainty can be supplied. By default, all of the above graphs are produced.
...	Optional arguments to be passed to MoE_gpairs, MoE_plotGate, MoE_plotCrit, MoE_plotLogLik, MoE_Similarity, MoE_Uncertainty, matplot, plot.mclustBIC and plot. In particular, the argument legendArgs to plot.mclustBIC can be passed to MoE_plotCrit.

Details

For more flexibility in plotting, use MoE_gpairs, MoE_plotGate, MoE_plotCrit, MoE_plotLogLik, MoE_Similarity, and MoE_Uncertainty directly.

Value

The visualisation according to what of the results of a fitted MoEClust model.

Note

Caution is advised producing generalised pairs plots when the dimension of the data is large.

Other types of plots are available by first calling as.Mclust on the fitted object, and then calling plot.Mclust on the results. These can be especially useful for univariate data.

Author(s)

Keefe Murphy - <[e-mail]>

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. <\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11634-019-00373-8")}>.

See Also

MoE_clust, MoE_stepwise, MoE_gpairs, MoE_plotGate, MoE_plotCrit, MoE_plotLogLik, MoE_Similarity, MoE_Uncertainty, as.Mclust, plot.Mclust

Examples

data(ais)
res <- MoE_clust(ais[,3:7], gating= ~ BMI, expert= ~ sex,
                 G=2, modelNames="EVE", network.data=ais)

# Plot the gating network
plot(res, what="gating", x.axis=ais$BMI, xlab="BMI")

# Plot the log-likelihood
plot(res, what="loglik", col="blue")

# Plot the uncertainty profile
plot(res, what="uncertainty", type="profile")

# Produce a generalised pairs plot
plot(res, what="gpairs")

# Produce a heatmap of the similarity matrix
plot(res, what="similarity")

# Modify the gpairs plot by passing arguments to MoE_gpairs()
plot(res, what="gpairs", response.type="density", varwidth=TRUE,
     data.ind=c(5,3,4,1,2), jitter=FALSE, show.counts=FALSE)

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