plot.mModelList {bgmm}

Plotting a graphical visualization of a model or a list of models

Description

The function plot.mModelList() creates a grid of panels and then plots a set of input fitted models in the consecutive panels. The plot.mModel() function is used to plot each single model.

Usage

## S3 method for class 'mModelList'
plot(x, ...)

Arguments

x

an object of the class mModelList.

...

graphical arguments that are passed to underlying plot() function.

Details

The argument x is a list of models. If these models differ both by component numbers and by the model structures, in the resulting grid of panels columns correspond to the different model structures while rows correspond to the different component numbers.

If considered models differ only by component numbers or only by the model structures, the grid of panels is as close to square as possible and consecutive panels contain consecutive models from the list of models x.

Author(s)

Przemyslaw Biecek

References

Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software.

See Also

plot.mModel

Examples

 simulated = simulateData(d=2, k=3, n=100, m=60, cov="0", within="E", n.labels=2)
 models1=mModelList(X=simulated$X, knowns=simulated$knowns, B=simulated$B, 
             kList=3:4, mean=c("D","E"), between="D", within="D", 
             cov="0", funct=belief)
 plot(models1)

## Do not run
## It could take more than one minute
# simulated = simulateData(d=2, k=3, n=300, m=60, cov="0", within="E", n.labels=2)
# 
# models1=mModelList(X=simulated$X, knowns=simulated$knowns, B=simulated$B, 
#             kList=3, mean=c("D","E"), between=c("D","E"), within=c("D","E"), 
#             cov=c("D","0"), funct=belief)
# plot(models1)
#
# models2 = beliefList(X=simulated$X, knowns=simulated$knowns, B=simulated$B,
#                kList=2:7, mean="D", between="D", within="E", cov="0")
# plot(models2)
# 
# models3 = beliefList(X=simulated$X, knowns=simulated$knowns, B=simulated$B,
#               kList=2:7, mean="D")
# plot(models3)