R: Classification and Visualization

# Classification and Visualization

## Documentation for package ‘klaR’ version 1.7-4

- DESCRIPTION file
- Package NEWS

## Help Pages

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.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