scoresPlot: Function to Plot a Scores Plot
In nlcv: Nested Loop Cross Validation

Description Usage Arguments Value Author(s)

Function to plot, for a given nested loop cross-validation object, a given classification technique and a given number of features used for the classification, the scores plot. This plot diplays the proportion of correctly-classified per sample across all runs of the nested loop cross-validation. The class membership of the samples is displayed using a colored strip (with legend below the plot).

1 2	scoresPlot(nlcvObj, tech, nfeat, plot = TRUE, barPlot = FALSE, layout = TRUE, main = NULL, sub = NULL, ...)

`nlcvObj`	Object of class 'nlcv' as produced by the `nlcv` function
`tech`	string denoting the classification technique used; one of 'dlda', 'bagg', 'pam', 'rf', or 'svm'.
`nfeat`	integer giving the number of features; this number should be part of the initial set of number of features that was specified during the nested loop cross-validation (`nFeatures` argument of the `nlcv` function)
`plot`	logical. If `FALSE`, nothing is plotted.
`barPlot`	Should a barplot be drawn (`TRUE`) or the alternative MCREstimate-type scores plot (the default, `FALSE`).
`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`)
`main`	Main title for the scores plot; if not supplied, 'Scores Plot' is used as a default
`sub`	Subtitle for the scores plot; if not supplied, the classification technique and the chosen number of features are displayed
`...`	Additional graphical parameters to pass to the plot function

A scores plot is displayed (for the device specified).

The function invisibly returns a named vector containing (for each sample) the proportion of times the sample was correctly classified (for a given technique and a given number of features used).

Willem Talloen and Tobias Verbeke

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