PDEnaiveBayes (version 0.2.8)

predict.PDEbayes: predict.PDEbayes

Description

Predict a classification with the Pareto Density estimated naive Bayes model (PDENB).

Usage

predict.PDEbayes(object, newdata, type = c("class", "response","prob"), ...)

Value

Cls

Numeric vector with predicted class associated with newdata.

Arguments

object

Model obtained from training routine in PDEnaiveBayes package.

newdata

[1:n,1:d] matrix of test data. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.

type

Optional parameter.

...

Gaussian: Optional: Default=TRUE). Assume gaussian distribution. Plausible: (Optional: TRUE: uses plausble bayesian theorem, FALSE non-plausible bayesian theorem Type: (Optional: default=1, 1 = original PDE, 2 = R native density estimation Threshold: Threshold for which the standard deviation cannot be smaller (default =1e-12) PlotIt: Optional: Default=FALSE, TRUE: Plots Likelihoods PlotCutOff: Optional: Scalar indicating how many features (starting from 1) should be plotted, or a numerical vector specifying the indices of the features to plot. Note: In the second case, avoid selecting too many features, as this may cause the plot to fail ParetoRadiusPerFeauture: Optional [1:d] numerical vector for pareto radii computed priorly, see ParetoRadius or {ParetoRadius_fast} cl: Optional: a cluster object, created by parallel, if given and ParetoRadiusPerFeauture missing, then ParetoRadiusPerFeauture is compputed multicore otherwise single core Robust: Optional: Default=FALSE, TRUE: robust estimation of mean and std in case of Gaussian=TRUE

Author

Michael Thrun

Details

The function is implemented in a way so that one can combine training and test data although it is intended to be applied on test data only.

See Also

Train_naiveBayes

Examples

if(requireNamespace("FCPS")){
V=FCPS::ClusterChallenge("Hepta",1000)
Data=V$Hepta
Cls=V$Cls
ind=1:length(Cls)
indtrain=sample(ind,800)
indtest=setdiff(ind,indtrain)

model=Train_naiveBayes(Data[indtrain,],Cls[indtrain],Gaussian=FALSE)
ClsTrain=model$ClsTrain
table(Cls[indtrain],ClsTrain)

ClsTest=predict.PDEbayes(object = model, newdata = Data[indtest,])
table(Cls[indtest],ClsTest)
}