k-Nearest Neighbour Cross-Validatory Classification
Usage
knn.cv(train, class, k=1, l=1, prob=FALSE)
Arguments
train
|
matrix or data frame of training set cases.
|
class
|
factor of true classifications of training set
|
k
|
number of neighbours considered.
|
l
|
minimum vote for definite decision, otherwise doubt . (More
precisely, less than k-l dissenting votes are allowed, even if k
is increased by ties.)
|
prob
|
If this is true, the proportion of the votes for the winning class
are returned as attribute prob .
|
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.
|
Description
k-nearest neighbour cross-validatory classification from training set. For
each row of the training set, the k nearest (in Euclidean distance) other
training set vectors are found, and the classification is decided by
majority vote, with ties broken at random. If there are ties for the
k
th nearest vector, all candidates are included in the vote.Value
factor of classifications of training set. doubt
will be returned as NA
.See Also
knn
Examples
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)))
knn.cv(train, cl, k=3, prob=TRUE)
attributes(.Last.value)