The plot method for flexmix-class objects gives a
rootogram or histogram of the posterior probabilities.
# S4 method for flexmix,missing
plot(x, y, mark = NULL, markcol = NULL,
col = NULL, eps = 1e-4, root = TRUE, ylim = TRUE, main = NULL, xlab = "",
ylab = "", as.table = TRUE, endpoints = c(-0.04, 1.04), ...)
An object of class "flexmix".
Not used.
Integer: mark posteriors of this component.
Color used for marking components.
Color used for the bars.
Posteriors smaller than eps are ignored.
If TRUE, a rootogram of the posterior probabilities
is drawn, otherwise a standard histogram.
A logical value or a numeric vector of length 2. 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 title of the plot.
Label of x-axis.
Label of y-axis.
Logical that controls the order in which panels
should be plotted: if FALSE (the default), panels are
drawn left to right, bottom to top (as in a graph); if
TRUE, left to right, top to bottom.
Vector of length 2 indicating the range of x-values that is
to be covered by the histogram. This applies only when
breaks is unspecified. In do.breaks, this specifies the interval
that is to be divided up.
Further graphical parameters for the lattice function histogram.
Friedrich Leisch and Bettina Gruen
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. Please note that the y-axis denotes the number of observations in each bar in any case.
Usually in each component a lot of observations have posteriors
close to zero, resulting in a high count for the corresponding
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. FlexMix: A general framework for finite mixture models and latent class regression in R. Journal of Statistical Software, 11(8), 2004. doi:10.18637/jss.v011.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, 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, 1405--1412. Physika Verlag, Heidelberg, Germany, 2004. ISBN 3-7908-1554-3.