plot-methods {flexmix} | R Documentation |
The plot
method for flexmix-class
objects gives a
rootogram or histogram of the posterior probabilities.
## S4 method for signature 'flexmix, missing': plot(x, y, mark=NULL, markcol="red", eps=1e-4, root=TRUE, ylim=TRUE, main=NULL, mfrow=NULL, ...)
x |
an object of class "flexmix" |
y |
not used |
mark |
integer, mark posteriors of this component |
markcol |
color used for marking components |
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 usual. |
main |
main title of the plot |
mfrow |
layout of the plot |
... |
further graphical parameters |
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.
Friedrich Leisch
Friedrich Leisch. FlexMix: A general framework for finite mixture models and latent class regression in R. Journal of Statistical Software, 11(8), 2004. http://www.jstatsoft.org/v11/i08/
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 Jaromir Antoch, editor, Compstat 2004 - Proceedings in Computational Statistics, pages 1405-1412. Physika Verlag, Heidelberg, Germany, 2004. ISBN 3-7908-1554-3.