PlotBayesianDecision2D: PlotBayesianDecision2D

In PDEnaiveBayes: Plausible Naive Bayes Classifier Using PDE

PlotBayesianDecision2D

Description

Plots estimation of decision boundary in a 2D slice of the data using the posteriors

Usage

PlotBayesianDecision2D(X, Y, Posteriors, Class = 1, NoBins,
CellColorsOrPallette, Showpoints = TRUE, xlim, ylim, xlab, ylab, main,
PlotIt = TRUE)

Arguments

X	Numeric vector with point coordinates of first dimension of data selection.
Y	Numeric vector with point coordinates of second dimension of data selection.
Posteriors	[1:n, 1:Class] matrix of posteriors.
Class	Optional,Integer defining which class to look at.
NoBins	Optional,Number of bins for class posteriori.
CellColorsOrPallette	Optional, Either a function defining the color palette of a character vector or character vector of length NoBins stating colors.
Showpoints	Optional, TRUE, points are displayed.
xlim	Optional,Numeric vector of length 2 stating limits of x axis.
ylim	Optional,Numeric vector of length 2 stating limits of y axis.
xlab	Optional,Character stating name of x axis.
ylab	Optional,Character stating name of y axis.
main	Optional, Character name of title
PlotIt	Optional, TRUE: prints GGPLOT2 object, FALSE: not shown plot.

Details

Boundaries are assumed to be zero for plotting.

Value

List of:

Mapping	List containing a map for colors, kernels and bin number.
GGobj	ggplot2 object containing 2D visualization of Posteriori.

Author(s)

Michael Thrun

Examples

Data = as.matrix(iris[,1:4])
Cls = as.numeric(iris[,5])

TrainIdx = c(17, 73, 46, 29, 68, 35, 131, 62, 132, 127, 71, 72, 
144, 99, 93, 13, 38, 21, 102, 53, 36, 111, 114, 96, 57, 74, 145, 
86, 3, 16, 52, 59, 140, 40, 122, 109, 6, 91, 79, 15, 108, 139, 
37, 76, 20, 115, 66, 28, 100, 117, 44, 78, 80, 150, 146, 142, 
9, 90, 45, 58, 134, 11, 87, 125, 141, 118, 136, 48, 124, 47, 
8, 27, 33, 92, 130, 54, 65, 104, 23, 98, 129, 123, 34, 128, 135, 
51, 64, 5, 94, 83, 42, 116, 101, 43, 7, 12, 82, 1, 84, 138, 2, 
56, 4, 106, 120)

TestIdx = c(60, 10, 75, 70, 81, 18, 97, 95, 67, 22, 55, 143, 
88, 24, 105, 26, 119, 31, 107, 63, 41, 61, 32, 147, 89, 14, 121, 
19, 113, 49, 126, 112, 25, 77, 137, 103, 50, 30, 149, 110, 39, 
69, 148, 85, 133)

TrainX = Data[TrainIdx, ]
TestX  = Data[TestIdx, ]
TrainY = Cls[TrainIdx]
TestY  = Cls[TestIdx]

VPDENB = Train_naiveBayes(Data = TrainX, Cls = TrainY, Plausible = FALSE)

PlotBayesianDecision2D(X = TrainX[, 1], Y = TrainX[, 2],
Posteriors = VPDENB$Posteriors, Class = 1)

PDEnaiveBayes documentation built on Nov. 17, 2025, 5:07 p.m.