Description Usage Arguments Value Author(s) See Also
plots for each classification technique and a given number of features used the mean misclassification rate (mcr) and its standard error across all runs of the nested loop cross-validation.
1 2 3 4 5 |
nlcvObj |
Object of class 'nlcv' as produced by the |
plot |
logical. If |
optimalDots |
Boolean indicating whether dots should be displayed on a panel below the graph to mark the optimal number of features for a given classification technique |
rescale |
if |
layout |
boolean indicating whether |
object |
Object of class 'mcrPlot' as produced by the function of the same name |
x |
Object of class 'summary.mcrPlot' as produced by the function of the same name |
digits |
number of digits to be passed to the default print method |
... |
Dots argument to pass additional graphical parameters
(such as |
An MCR plot is output to the device of choice. The dots represent the mean MCR across runs. The vertical lines below and above the dots represent the standard deviation of the MCR values across runs.
Below the plot coloured solid dots (one for each classification technique) indicate for which number of features a given technique reached its minimum MCR.
The function invisibly returns an object of class mcrPlot
which
is a list with components
meanMcrMatrix |
matrix with for each number of features (rows) and classification technique (columns) the mean of the MCR values across all runs of the nlcv procedure. |
sdMcrMatrix |
matrix with for each number of features (rows) and classification technique (columns) the sd of the MCR values across all runs of the nlcv procedure. |
The summary
method for the mcrPlot
object returns a matrix with for
each classification technique, the optimal number of features as well as the
associated mean MCR and standard deviation of the MCR values.
Willem Talloen and Tobias Verbeke
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