RDocumentation
Moon
Learn R
Search all packages and functions
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.
Copy Link
Copy
Link to current version
Version
Version
1.2-1
1.0
Down Chevron
Install
install.packages('rknn')
Monthly Downloads
36
Version
1.2-1
License
GPL (>= 2)
Maintainer
Shengqiao Li
Last Published
June 8th, 2015
Functions in rknn (1.2-1)
Search functions
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