Misclassification Rate Plot

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Description

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.

Usage

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mcrPlot(nlcvObj, plot = TRUE, optimalDots = TRUE, rescale = FALSE, layout = TRUE, ...)
## S3 method for class 'mcrPlot'
summary(object, ...)
## S3 method for class 'summary.mcrPlot'
print(x, digits = 2, ...)

Arguments

nlcvObj

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

plot

logical. If FALSE, nothing is plotted.

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

layout

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

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 main) to the plot function

Value

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.

Author(s)

Willem Talloen and Tobias Verbeke

See Also

nlcv