knn3 | R Documentation |
$k$-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 |
knn3
is essentially the same code as ipredknn
and knn3Train
is a copy of knn
. The underlying C
code from the class
package has been modified to return the vote
percentages for each class (previously the percentage for the winning class
was returned).
An object of class knn3
. See predict.knn3
.
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
Andre Williams
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)
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