$k$-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)

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 `NA`

s.

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 `k`

th largest are included. If false, a random selection of
distances equal to the `k`

th is chosen to use exactly `k`

neighbours.

An object of class `knnreg`

. See `predict.knnreg`

.

`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.

# NOT RUN { 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)) # }