knnreg: k-Nearest Neighbour Regression

Description Usage Arguments Details Value Author(s) Examples

Description

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

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
knnreg(x, ...)

## 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, ...)

## S3 method for class '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.

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.

Value

An object of class knnreg. See predict.knnreg.

Author(s)

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

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
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))

caret documentation built on Oct. 9, 2021, 5:09 p.m.