Classification and Visualization

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Documentation for package ‘klaR’ version 0.6-15

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B C D E F G H K L M N P Q R S T U W X

-- B --

b.scal Calculation of beta scaling parameters
B3 West German Business Cycles 1955-1994
benchB3 Benchmarking on B3 data
betascale Scale membership values according to a beta scaling

-- C --

calc.trans Calculation of transition probabilities
centerlines Lines from classborders to the center
classscatter Classification scatterplot matrix
cond.index Calculation of Condition Indices for Linear Regression
corclust Function to identify groups of highly correlated variables for removing correlated features from the data for further analysis.
countries Socioeconomic data for the most populous countries.
cvtree Extracts variable cluster IDs

-- D --

dkernel Estimate density of a given kernel
drawparti Plotting the 2-d partitions of classification methods

-- E --

e.scal Function to calculate e- or softmax scaled membership values
EDAM Computation of an Eight Direction Arranged Map
errormatrix Tabulation of prediction errors by classes

-- F --

friedman.data Friedman's classification benchmark data

-- G --

GermanCredit Statlog German Credit
greedy.wilks Stepwise forward variable selection for classification
greedy.wilks.default Stepwise forward variable selection for classification
greedy.wilks.formula Stepwise forward variable selection for classification

-- H --

hmm.sop Calculation of HMM Sum of Path

-- K --

kmodes K-Modes Clustering

-- L --

level_shardsplot Plotting Eight Direction Arranged Maps or Self-Organizing Maps
loclda Localized Linear Discriminant Analysis (LocLDA)
loclda.data.frame Localized Linear Discriminant Analysis (LocLDA)
loclda.default Localized Linear Discriminant Analysis (LocLDA)
loclda.formula Localized Linear Discriminant Analysis (LocLDA)
loclda.matrix Localized Linear Discriminant Analysis (LocLDA)
locpvs Pairwise variable selection for classification in local models

-- M --

meclight Minimal Error Classification
meclight.data.frame Minimal Error Classification
meclight.default Minimal Error Classification
meclight.formula Minimal Error Classification
meclight.matrix Minimal Error Classification

-- N --

NaiveBayes Naive Bayes Classifier
NaiveBayes.default Naive Bayes Classifier
NaiveBayes.formula Naive Bayes Classifier
nm Nearest Mean Classification
nm.data.frame Nearest Mean Classification
nm.default Nearest Mean Classification
nm.formula Nearest Mean Classification
nm.matrix Nearest Mean Classification

-- P --

partimat Plotting the 2-d partitions of classification methods
partimat.data.frame Plotting the 2-d partitions of classification methods
partimat.default Plotting the 2-d partitions of classification methods
partimat.formula Plotting the 2-d partitions of classification methods
partimat.matrix Plotting the 2-d partitions of classification methods
plineplot Plotting marginal posterior class probabilities
plot.corclust Function to identify groups of highly correlated variables for removing correlated features from the data for further analysis.
plot.EDAM Plotting Eight Direction Arranged Maps or Self-Organizing Maps
plot.NaiveBayes Naive Bayes Plot
plot.rda Regularized Discriminant Analysis (RDA)
plot.stepclass Stepwise variable selection for classification
plot.woe Plot information values
predict.loclda Localized Linear Discriminant Analysis (LocLDA)
predict.locpvs predict method for locpvs objects
predict.meclight Prediction of Minimal Error Classification
predict.NaiveBayes Naive Bayes Classifier
predict.pvs predict method for pvs objects
predict.rda Regularized Discriminant Analysis (RDA)
predict.sknn Simple k Nearest Neighbours Classification
predict.svmlight Interface to SVMlight
predict.woe Weights of evidence
print.greedy.wilks Stepwise forward variable selection for classification
print.kmodes K-Modes Clustering
print.loclda Localized Linear Discriminant Analysis (LocLDA)
print.meclight Minimal Error Classification
print.pvs Pairwise variable selection for classification
print.rda Regularized Discriminant Analysis (RDA)
print.stepclass Stepwise variable selection for classification
print.woe Weights of evidence
pvs Pairwise variable selection for classification
pvs.default Pairwise variable selection for classification
pvs.formula Pairwise variable selection for classification

-- Q --

quadplot Plotting of 4 dimensional membership representation simplex

-- R --

rda Regularized Discriminant Analysis (RDA)
rda.default Regularized Discriminant Analysis (RDA)
rda.formula Regularized Discriminant Analysis (RDA)

-- S --

shardsplot Plotting Eight Direction Arranged Maps or Self-Organizing Maps
sknn Simple k nearest Neighbours
sknn.data.frame Simple k nearest Neighbours
sknn.default Simple k nearest Neighbours
sknn.formula Simple k nearest Neighbours
sknn.matrix Simple k nearest Neighbours
stepclass Stepwise variable selection for classification
stepclass.default Stepwise variable selection for classification
stepclass.formula Stepwise variable selection for classification
svmlight Interface to SVMlight
svmlight.data.frame Interface to SVMlight
svmlight.default Interface to SVMlight
svmlight.file Interface to SVMlight
svmlight.formula Interface to SVMlight
svmlight.matrix Interface to SVMlight

-- T --

triframe Barycentric plots
trigrid Barycentric plots
trilines Barycentric plots
triperplines Barycentric plots
triplot Barycentric plots
tripoints Barycentric plots
tritrafo Barycentric plots

-- U --

ucpm Uschi's classification performance measures

-- W --

woe Weights of evidence
woe.default Weights of evidence
woe.formula Weights of evidence

-- X --

xtractvars Variable clustering based variable selection