DiagnosticAbility4Classifiers: DiagnosticAbility4Classifiers
In Mthrun/DataVisualizations: Visualizations of High-Dimensional Data

View source: R/DiagnosticAbility4Classifiers.R

DiagnosticAbility4Classifiers

R Documentation

DiagnosticAbility4Classifiers

Description

DiagnosticAbility4Classifiers as applied in [...].

Usage

DiagnosticAbility4Classifiers(TrueCondition_Cls, ManyPredictedCondition_Cls,

NamesOfConditions = NULL, PlotType = "PRC", xlab = "True Positive Rate",

ylab = "False Positive Rate", main = "ROC Space",

Colors, LineColor = NULL, Size = 8, LineWidth = 1,

LineType = NULL, Showgrid = TRUE, SaveIt = FALSE)

Arguments

`TrueCondition_Cls`	[1:n] numeric vector of k classes (true classification), preferably of the testset
`ManyPredictedCondition_Cls`	[1:n,1:c] every col c is a Cls of one specific condition of the classifier trying to reproduce the classification (preferably on a test set)
`NamesOfConditions`	[1:c] character vector of c conditions, sets names of legend and the points
`PlotType`	possible are 'ROC':Receiver operating characteristic. 'PRC': Precision Recall, and 'SenSpec':Sensitivity-Specifity Plot
`xlab`	Optional, string
`ylab`	Optional, string
`main`	Optional, string
`Colors`	Optional, string
`LineColor`	Optional, name of color, then all points are connected by a curve
`Size`	Optional, number defining the Size of the curve
`LineWidth`	Optional, number defining the width of the curve
`LineType`	Optional, string defining the type of the curve
`Showgrid`	Optional, boolean
`SaveIt`	Optional, boolean, if true saves plot as html

Details

For unbalanced binary classes PRC should be preferred and not ROC [Saito/Rehmsmeier, 2016].

Value

If it is a LIST, use

`Plot`	plotly handler
`X`	[1:c] vector of xaxis values
`Y`	[1:c] vector of y axis values

Note

Currently only for binary classifiers developed

Author(s)

Michael Thrun

References

[|] :Determination of CD43 and CD200 surface expression improves accuracy of B-cell lymphoma immunophenotyping, 2020.

[Saito/Rehmsmeier, 2016] Saito, Takaya and Rehmsmeier, Marc: The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets, PlosOne, https://doi.org/10.1371/journal.pone.0118432, 2016.