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

```
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, ...)
```

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`

.

Invisible matrix of posterior probabilities `z`

from
`mclustsummary`

.

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

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

# NOT RUN { 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. # }