PlotLikelihoods: PlotLikelihoods

In PDEnaiveBayes: Plausible Naive Bayes Classifier Using PDE

PlotLikelihoods

Description

Plots the Likelihoods per feature.

Usage

PlotLikelihoods(Likelihoods, Data, PlausibleLikelihoods=NULL,Epsilon=NULL,
PlausibleCenters=NULL,PlotCutOff=4,xlim)

Arguments

Likelihoods	List with Likelihoods.
Data	Numeric matrix with data.
PlausibleLikelihoods	List with plausible Likelihoods.
Epsilon	Numeric scalar defining epsilon fo plausible likelihoods.
PlausibleCenters	Numeric vector [1:k] plausible centers used to compute plausible likelihoods.
PlotCutOff	scalar defining the how many feature starting from 1 should be plotted or numerical vector defining the index of features to be plotted in second case should not be too many otherwise plot yields an error.
xlim	Numeric vector of length 2 stating limits of x axis.

Details

Boundaries are assumed to be zero for plotting.

Value

No return value.

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)

PlotLikelihoods(Likelihoods = VPDENB$Model$ListOfLikelihoods, Data = TrainX)

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