knnreg {caret} | R Documentation |
$k$-nearest neighbour regression that can return the average value for the neighbours.
## Default S3 method: knnreg(x, ...) ## S3 method for class 'formula' knnreg(formula, data, subset, na.action, k = 5, ...) ## S3 method for class 'matrix' knnreg(x, y, k = 5, ...) ## S3 method for class 'data.frame' knnreg(x, y, k = 5, ...) knnregTrain(train, test, y, k = 5, use.all=TRUE)
formula |
a formula of the form |
data |
optional data frame containing the variables in the model formula. |
subset |
optional vector specifying a subset of observations to be used. |
na.action |
function which indicates what should happen when
the data contain |
k |
number of neighbours considered. |
x |
a matrix or data frame of training set predictors. |
y |
a numeric vector of outcomes. |
... |
additional parameters to pass to |
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. |
use.all |
controls handling of ties. If true, all distances equal to the |
knnreg
is similar to ipredknn
and knnregTrain
is a modification of knn
. The underlying
C code from the class
package has been modified to return average outcome.
An object of class knnreg
. See predict.knnreg
.
knn
by W. N. Venables and B. D. Ripley and
ipredknn
by
Torsten.Hothorn <Torsten.Hothorn@rzmail.uni-erlangen.de>,
modifications by Max Kuhn and Chris Keefer
data(BloodBrain) inTrain <- createDataPartition(logBBB, p = .8)[[1]] trainX <- bbbDescr[inTrain,] trainY <- logBBB[inTrain] testX <- bbbDescr[-inTrain,] testY <- logBBB[-inTrain] fit <- knnreg(trainX, trainY, k = 3) plot(testY, predict(fit, testX))