R: Ordered posterior plots

weightplots {fpc} | R Documentation

## 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 c.hennig@ucl.ac.uk http://www.homepages.ucl.ac.uk/~ucakche/

### 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.

[Package fpc version 2.1-10 Index]