k-Nearest Neighbour Regression

$k$-nearest neighbour regression that can return the average value for the neighbours.

knnreg(x, ...)

# S3 method for default
knnreg(x, ...)

# S3 method for formula
knnreg(formula, data, subset, na.action, k = 5, ...)

# S3 method for matrix
knnreg(x, y, k = 5, ...)

# S3 method for data.frame
knnreg(x, y, k = 5, ...)

# S3 method for knnreg
print(x, ...)

knnregTrain(train, test, y, k = 5, use.all = TRUE)

Arguments

x	a matrix or data frame of training set predictors.
...	additional parameters to pass to knnregTrain.
formula	a formula of the form lhs ~ rhs where lhs is the response variable and rhs a set of predictors.
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 NAs.
k	number of neighbours considered.
y	a numeric vector of outcomes.
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 kth largest are included. If false, a random selection of distances equal to the kth is chosen to use exactly k neighbours.

Value

An object of class knnreg. See predict.knnreg.

Details

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.

Examples

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))