## S3 method for class 'formula':
knn3(formula, data, subset, na.action, k = 5, ...) ## S3 method for class 'matrix':
knn3(x, y, k = 5, ...)
## S3 method for class 'data.frame':
knn3(x, y, k = 5, ...)
knn3Train(train, test, cl, k=1, l=0, prob = TRUE, use.all=TRUE)
lhs ~ rhs
where lhs
is the response variable and rhs
a set of
predictors.NA
s.knn3Train
. However, passing
prob = FALSE
will be over--ridden.doubt
. (More
precisely, less than k-l
dissenting votes are allowed, even if k
is increased by ties.)prob
.k
th
largest are included. If false, a random selection of distances
equal to the k
th is chosen to use exactly k
neighbours.knn3
. See predict.knn3
.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).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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