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

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

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 `k`

th
largest are included. If false, a random selection of distances
equal to the `k`

th is chosen to use exactly `k`

neighbours.

Factor of classifications of training set. `doubt`

will be returned as `NA`

.

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
`k`

th nearest vector, all candidates are included in the vote.

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.

# 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) # }