k-Nearest Neighbour Classification
$k$-nearest neighbour classification that can return class votes for all classes.
knn3(x, ...) ## S3 method for class 'formula' knn3(formula, data, subset, na.action, k = 5, ...) ## S3 method for class 'data.frame' knn3(x, y, k = 5, ...) ## S3 method for class 'matrix' knn3(x, y, k = 5, ...) ## S3 method for class 'knn3' print(x, ...) knn3Train(train, test, cl, k = 1, l = 0, prob = TRUE, use.all = TRUE)
x |
a matrix of training set predictors |
... |
additional parameters to pass to |
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. |
y |
a factor vector of training set classes |
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. |
cl |
factor of true classifications of training set |
l |
minimum vote for definite decision, otherwise |
prob |
If this is true, the proportion of the votes for each class are
returned as attribute |
use.all |
controls handling of ties. If true, all distances equal to
the |
An object of class knn3. See predict.knn3.
irisFit1 <- knn3(Species ~ ., iris)
irisFit2 <- knn3(as.matrix(iris[, -5]), iris[,5])
data(iris3)
train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
knn3Train(train, test, cl, k = 5, prob = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.