rknn v1.2-1

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Random KNN Classification and Regression

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.

Functions in rknn

Name Description
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
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Details

Type Package
Date 2015-06-07
License GPL (>= 2)
LazyLoad yes
NeedsCompilation yes
Packaged 2015-06-07 11:27:21 UTC; Tiger
Repository CRAN
Date/Publication 2015-06-09 00:14:51

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