fpc: weightplots – R documentation

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fpc

weightplots

Ordered posterior plots

Description

Ordered posterior plots for Gaussian mixture components, see Hennig (2010).

Usage

weightplots(z, clusternumbers="all", clustercol=2,
                        allcol=grey(0.2+((1:ncol(z))-1)*
                          0.6/(ncol(z)-1)),
                        lty=rep(1,ncol(z)),clusterlwd=3,
                        legendposition="none",
                        weightcutoff=0.01,ask=TRUE, ...)

Arguments

`z`	matrix with rows corresponding to observations and columns corresponding to mixture components. Entries are probabilities that an observation has been generated by a mixture component. These will normally be estimated a posteriori probabilities, as generated as component `z` of the output object from `summary.mclustBIC`.
`clusternumbers`	`"all"` or vector of integers. Numbers of components for which plots are drawn.
`clustercol`	colour used for the main components for which a plot is drawn.
`allcol`	colours used for respective other components in plots in which they are not main components.
`lty`	line types for components.
`clusterlwd`	numeric. Line width for main component.
`legendposition`	`"none"` or vector with two coordinates in the plot, where a legend should be printed.
`weightcutoff`	numeric between 0 and 1. Observations are only taken into account for which the posterior probability for the main component is larger than this.
`ask`	logical. If `TRUE`, it sets `par(ask=TRUE)` in the beginning and `par(ask=FALSE)` after all plots were showed.
`...`	further parameters to be passed on to `legend`.

Details

Shows posterior probabilities for observations belonging to all mixture components on the y-axis, with points ordered by posterior probability for main component.

Value

Invisible matrix of posterior probabilities z from mclustsummary.

Author(s)

Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en/

References

Hennig, C. (2010) Methods for merging Gaussian mixture components, Advances in Data Analysis and Classification, 4, 3-34.

Examples

require(mclust)
  require(MASS)
  data(crabs)
  dc <- crabs[,4:8]
  cm <- mclustBIC(crabs[,4:8],G=9,modelNames="EEE")
  scm <- summary(cm,crabs[,4:8])
  weightplots(scm$z,clusternumbers=1:3,ask=FALSE)
  weightplots(scm$z,clusternumbers=1:3,allcol=1:9, ask=FALSE,
              legendposition=c(5,0.7))
# Remove ask=FALSE to have time to watch the plots.

fpc

Flexible Procedures for Clustering

v2.2-9

GPL

Authors

Christian Hennig <christian.hennig@unibo.it>

Initial release

2020-12-06