FNN: knn.reg – R documentation

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FNN

knn.reg

k Nearest Neighbor Regression

Description

k-nearest neighbor regression

Usage

knn.reg(train, test = NULL, y, k = 3, algorithm=c("kd_tree", 
        "cover_tree", "brute"))

Arguments

`train`	matrix or data frame of training set cases.
`test`	matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case. If not supplied, cross-validataion will be done.
`y`	reponse of each observation in the training set.
`k`	number of neighbours considered.
`algorithm`	nearest neighbor search algorithm.

Details

If test is not supplied, Leave one out cross-validation is performed and R-square is the predicted R-square.

Value

knn.reg returns an object of class "knnReg" or "knnRegCV" if test data is not supplied.

The returnedobject is a list containing at least the following components:

`call`	the match call.
`k`	number of neighbours considered.
`n`	number of predicted values, either equals test size or train size.
`pred`	a vector of predicted values.
`residuals`	predicted residuals. `NULL` if `test` is supplied.
`PRESS`	the sums of squares of the predicted residuals. `NULL` if `test` is supplied.
`R2Pred`	predicted R-square. `NULL` if `test` is supplied.

Note

The code for “VR” nearest neighbor searching is taken from class source

Author(s)

Shengqiao Li. To report any bugs or suggestions please email: lishengqiao@yahoo.com.

Examples

if(require(chemometrics)){
    data(PAC);
    pac.knn<- knn.reg(PAC$X, y=PAC$y, k=3);
    
    plot(PAC$y, pac.knn$pred, xlab="y", ylab=expression(hat(y)))
  }

FNN

Fast Nearest Neighbor Search Algorithms and Applications

v1.1.3

GPL (>= 2)

Authors

Alina Beygelzimer, Sham Kakadet and John Langford (cover tree library), Sunil Arya and David Mount (ANN library 1.1.2 for the kd-tree approach), Shengqiao Li

Initial release

2019-02-15