mcrPlot: Misclassification Rate Plot

Description Usage Arguments Value Author(s) See Also

View source: R/mcrPlot.R


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.


mcrPlot(nlcvObj, plot = TRUE, optimalDots = TRUE, rescale = FALSE, layout = TRUE, ...)



Object of class 'nlcv' as produced by the nlcv function


logical. If FALSE, nothing is plotted.


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


if TRUE, the upper limit of y-axis is dependent on the data (maximum mcr value); defaults to FALSE which implies limits c(0,1)


boolean indicating whether mcrPlot should prespecify a layout for a single plot (default, TRUE) or whetherl the user takes care of the layout (FALSE)


Dots argument to pass additional graphical parameters (such as main) to the plot function


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:

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

See Also


nlcv documentation built on July 2, 2018, 1:03 a.m.