plot-methods | 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 = NULL,
col = NULL, eps = 1e-4, root = TRUE, ylim = TRUE, main = NULL, xlab = "",
ylab = "", as.table = TRUE, endpoints = c(-0.04, 1.04), ...)
x |
An object of class |
y |
Not used. |
mark |
Integer: mark posteriors of this component. |
markcol |
Color used for marking components. |
col |
Color used for the bars. |
eps |
Posteriors smaller than |
root |
If |
ylim |
A logical value or a numeric vector of length 2. If
|
main |
Main title of the plot. |
xlab |
Label of x-axis. |
ylab |
Label of y-axis. |
as.table |
Logical that controls the order in which panels
should be plotted: if |
endpoints |
Vector of length 2 indicating the range of x-values that is
to be covered by the histogram. This applies only when
|
... |
Further graphical parameters for the lattice function histogram. |
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 and Bettina Gruen
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.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.