k Nearest Neighbor Regression

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Description

k-nearest neighbor regression

Usage

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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: shli@stat.wvu.edu.

See Also

knn.

Examples

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  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)))
  }