MoEClust (version 1.6.0)

plot.MoEClust: 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 MoEClust
plot(x,
     what = c("gpairs", "gating", "criterion", "loglik", "similarity", "uncertainty"),
     ...)

Value

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

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.

Author

Keefe Murphy - <>

Details

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

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. <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, show.dens=TRUE,
     data.ind=c(5,3,4,1,2), jitter=FALSE, show.counts=FALSE)