knn.reg {FNN} | R Documentation |
k-nearest neighbor regression
knn.reg(train, test = NULL, y, k = 3, algorithm=c("kd_tree",
"cover_tree", "brute"))
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. |
If test is not supplied, Leave one out cross-validation is performed and R-square is the predicted R-square.
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. |
PRESS |
the sums of squares of the predicted residuals. |
R2Pred |
predicted R-square. |
The code for “VR” nearest neighbor searching is taken from class
source
Shengqiao Li. To report any bugs or suggestions please email: lishengqiao@yahoo.com
knn
.
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)))
}