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rknn (version 1.2-1)

Random KNN Classification and Regression

Description

Random knn classification and regression are implemented. Random knn based feature selection methods are also included. The approaches are mainly developed for high-dimensional data with small sample size.

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Version

Install

install.packages('rknn')

Monthly Downloads

34

Version

1.2-1

License

GPL (>= 2)

Maintainer

Shengqiao Li

Last Published

June 8th, 2015

Functions in rknn (1.2-1)

plot rknn support

Plot Function for Support Criterion
lambda

Compute Number of Silent Features
normalize

Data Normalization
cv.coef

Coefficient of Variation
print.rknn

Print method for Random KNN
rknnSupport

Support Criterion
eta

Coverage Probability
rknn-package

Random KNN Classification and Regression
bestset

Extract the Best Subset of Feature from Selection Process
rknn

Random KNN Classification and Regression
internal functions

Random KNN Internal Functions
PRESS

Predicted Residual Sum of Squares
rsqp

Predicted R-square
print.rknnBE

Print Method for Recursive Backward Elimination Feature Selection
plot backward elimination

Plot Function for Recursive Backward Elimination Feature Selection
varUsed

Features Used or Not Used in Random KNN
r

Choose number of KNNs
fitted

Extract Model Fitted Values
print.rknnSupport

Print Method for Random KNN Support Criterion
rknnBeg

Backward Elimination Feature Selection with Random KNN
predicted

Prediced Value From a Linear Model
confusion

Classification Confusion Matrix and Accuracy