# plot-methods

##### Rootogram of Posterior Probabilities

The `plot`

method for `flexmix-class`

objects gives a
rootogram or histogram of the posterior probabilities.

##### Usage

```
## S3 method for class 'flexmix,missing':
plot(x, eps=1e-4, root=TRUE,
ylim=TRUE, main=NULL, mfrow=NULL, ...)
```

##### Arguments

- x
- an object of class
`"flexmix"`

- eps
- posteriors smaller than
`eps`

are ignored - root
- if
`TRUE`

, a rootogram of the posterior probabilities is drawn, otherwise a standard historgram - ylim
- A logical value or a numeric vector of length n2. If
`TRUE`

, the y axes of all rootograms are aligned to have the same limits, if`FALSE`

each y axis is scaled separately. If a numeric vector is specified it is used as us - main
- main title of the plot
- mfrow
- layout of the plot
- ...
- further graphical parameters

##### Details

For each mixture component a rootogram or histogram of the posterior probabilities of all observations is drawn. Rootograms are very similar to histograms, the only difference is that the height of the bars correspond to square roots of counts rather than the counts themselves, hence low counts are more visible and peaks less emphasized.

Usually in each component a lot of observations have posteriors
close to zero, resulting in a high count for the corresponing
bin in the rootogram which obscures the information in the other
bins. To avoid this problem, all probabilities with a posterior below
`eps`

are ignored.

A peak at probability one indicates that a mixture component is well seperated from the other components, while no peak at one and/or significant mass in the middle of the unit interval indicates overlap with other components.

##### References

Jeremy Tantrum, Alejandro Murua and Werner Stuetzle. Assessment and pruning of hierarchical model based clustering. Proceedings of the 9th ACM SIGKDD international conference on Knowledge Discovery and Data Mining, pages 197-205. ACM Press, New York, NY, USA, 2003.

Friedrich Leisch. Exploring the structure of mixture model components. In Compstat 2004 -- Proceedings in Computational Statistics, 2004. Accepted for publication.

*Documentation reproduced from package flexmix, version 0.9-0, License: GPL version 2.*