plot,cloutput-method: Probability plot
In chbernau/CMA: Synthesis of microarray-based classification
Description
Arguments
Value
Note
Author(s)
References
See Also
Description
A popular way of visualizing the output of classifier
is to plot, separately for each class, the predicted
probability of each predicted observations for the respective class.
For this purpose, the plot area is divided into K
parts, where K is the number of classes.
Predicted observations are assigned, according to their
true class, to one of those parts. Then, for each part
and each predicted observation, the predicted probabilities
are plotted, displayed by coloured dots, where each
colour corresponds to one class.
Arguments
x
An object of class cloutput whose slot
probmatrix does not contain any missing value,
i.e. probability estimations are provided by the classifier.
main
A title for the plot (character).
Value
No return.
Note
The plot usually only makes sense if a sufficiently large numbers
of observations has been classified. This is usually achieved
by running the classifier on several learningsets
with the method classification. The output can
then be processed via join to obtain an object
of class cloutput to which this method can be applied.
Author(s)
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
References
Slawski, M. Daumer, M. Boulesteix, A.-L. (2008)
CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data.
BMC Bioinformatics 9: 439
See Also
chbernau/CMA documentation built on May 17, 2019, 12:04 p.m.
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Description Arguments Value Note Author(s) References See Also
Description
A popular way of visualizing the output of classifier
is to plot, separately for each class, the predicted
probability of each predicted observations for the respective class.
For this purpose, the plot area is divided into K
parts, where K is the number of classes.
Predicted observations are assigned, according to their
true class, to one of those parts. Then, for each part
and each predicted observation, the predicted probabilities
are plotted, displayed by coloured dots, where each
colour corresponds to one class.
Arguments
x |
An object of class |
main |
A title for the plot (character). |
Value
No return.
Note
The plot usually only makes sense if a sufficiently large numbers
of observations has been classified. This is usually achieved
by running the classifier on several learningsets
with the method classification. The output can
then be processed via join to obtain an object
of class cloutput to which this method can be applied.
Author(s)
Martin Slawski ms@cs.uni-sb.de
Anne-Laure Boulesteix boulesteix@ibe.med.uni-muenchen.de
References
Slawski, M. Daumer, M. Boulesteix, A.-L. (2008) CMA - A comprehensive Bioconductor package for supervised classification with high dimensional data. BMC Bioinformatics 9: 439
See Also
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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