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.

reponse of each observation in the training set.

number of neighbours considered.

algorithm

nearest neighbor search algorithm.

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.

number of neighbours considered.

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.

Details

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

Examples

Run this code

# NOT RUN {
  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)))
  } 
# }