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Plot cluster labels

plot.dpmixgpd_cluster_labels.Rd

Visualize representative cluster sizes, assignment certainty, or cluster-specific response summaries. For type = "summary", the response view is shown as boxplots ordered by cluster size or label. When x comes from predict(..., newdata = ...), only clusters represented in the new sample are displayed.

Usage

# S3 method for class 'dpmixgpd_cluster_labels'
plot(
  x,
  type = c("sizes", "certainty", "summary"),
  top_n = 5L,
  order_by = c("size", "label"),
  plotly = getOption("CausalMixGPD.plotly", FALSE),
  ...
)

Arguments

x

Cluster labels object.

type

Plot type:

  • "sizes": bar chart of representative cluster sizes

  • "certainty": assignment certainty distribution

  • "summary": cluster-specific response boxplots

top_n

Number of populated representative clusters to display for type = "sizes" or type = "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 method visualizes the representative partition stored in a dpmixgpd_cluster_labels object. The sizes view emphasizes the empirical distribution of the selected clusters, the certainty view summarizes the assignment scores \(\max_k p_{ik}\), and the summary view compares the attached response data across representative clusters.

For new-data prediction, the plots are always interpreted relative to the representative training clusters. That is why only clusters observed in the predicted sample are shown even though the training partition may contain additional occupied groups.

See also

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

Other cluster workflow: dpmgpd.cluster(), dpmix.cluster(), plot.dpmixgpd_cluster_bundle(), plot.dpmixgpd_cluster_fit(), 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()