class (version 7.3-17)

knn.cv: k-Nearest Neighbour Cross-Validatory Classification

Description

k-nearest neighbour cross-validatory classification from training set.

Usage

knn.cv(train, cl, k = 1, l = 0, prob = FALSE, use.all = TRUE)

Arguments

train

matrix or data frame of training set cases.

cl

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 kth largest are included. If false, a random selection of distances equal to the kth is chosen to use exactly k neighbours.

Value

Factor of classifications of training set. doubt will be returned as NA.

Details

This uses leave-one-out cross validation. For each row of the training set train, 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 kth nearest vector, all candidates are included in the vote.

References

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

knn

Examples

# NOT RUN {
train <- rbind(iris3[,,1], iris3[,,2], iris3[,,3])
cl <- factor(c(rep("s",50), rep("c",50), rep("v",50)))
knn.cv(train, cl, k = 3, prob = TRUE)
attributes(.Last.value)
# }