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Plot a cluster fit

plot.dpmixgpd_cluster_fit.Rd

Visualize either the posterior similarity matrix, the posterior number of occupied clusters, the size distribution of the representative clusters, or cluster-specific response summaries.

Usage

# S3 method for class 'dpmixgpd_cluster_fit'
plot(
  x,
  which = c("psm", "k", "sizes", "summary"),
  burnin = NULL,
  thin = NULL,
  psm_max_n = 2000L,
  top_n = 5L,
  order_by = c("size", "label"),
  plotly = getOption("CausalMixGPD.plotly", FALSE),
  ...
)

Arguments

x

A cluster fit.

which

Plot type:

  • "psm": posterior similarity matrix heatmap

  • "k": posterior number of occupied clusters

  • "sizes": bar chart of representative cluster sizes

  • "summary": cluster-specific response summaries

burnin

Number of initial posterior draws to discard.

thin

Keep every thin-th posterior draw.

psm_max_n

Maximum training sample size allowed for PSM plotting.

top_n

Number of populated representative clusters to display for which = "sizes" or which = "summary". Use NULL to display all populated clusters.

order_by

Ordering rule for cluster displays:

  • "size": decreasing cluster size

  • "label": ascending cluster label

plotly

Logical; if TRUE, convert the ggplot2 output to a plotly / htmlwidget representation via .wrap_plotly(). Defaults to getOption("CausalMixGPD.plotly", FALSE).

...

Unused.

Value

A ggplot2 object or a plotly/htmlwidget object when plotly = TRUE.

Details

This plot method exposes the main posterior diagnostics for clustering. The which = "k" view tracks the number of occupied clusters across retained draws, which = "psm" visualizes pairwise co-clustering probabilities, which = "sizes" displays the size profile of the representative partition, and which = "summary" shows response summaries conditional on the selected representative labels.

The representative partition is obtained from predict.dpmixgpd_cluster_fit() using Dahl's least-squares rule. As a result, the sizes and summary views describe that representative clustering rather than the full posterior distribution over partitions.

See also

predict.dpmixgpd_cluster_fit(), summary.dpmixgpd_cluster_fit(), plot.dpmixgpd_cluster_psm(), plot.dpmixgpd_cluster_labels().

Other cluster workflow: dpmgpd.cluster(), dpmix.cluster(), plot.dpmixgpd_cluster_bundle(), plot.dpmixgpd_cluster_labels(), plot.dpmixgpd_cluster_psm(), predict.dpmixgpd_cluster_fit(), print.dpmixgpd_cluster_bundle(), print.dpmixgpd_cluster_fit(), print.dpmixgpd_cluster_labels(), print.dpmixgpd_cluster_psm(), summary.dpmixgpd_cluster_bundle(), summary.dpmixgpd_cluster_fit(), summary.dpmixgpd_cluster_labels(), summary.dpmixgpd_cluster_psm()