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:
meanMcrMatrixmatrix with for each number of features (rows) and classification technique (columns) the mean of the MCR values across all runs of the nlcv procedure.
sdMcrMatrixmatrix 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.