## S3 method for class 'formula': knn3(formula, data, subset, na.action, k = 5, ...)
## S3 method for class 'matrix': knn3(x, y, k = 5, ...)
knn3Train(train, test, cl, k=1, l=0, prob = TRUE, use.all=TRUE)
lhs ~ rhswhere
lhsis the response variable and
rhsa set of predictors.
knn3Train. However, passing
prob = FALSEwill be over--ridden.
doubt. (More precisely, less than
k-ldissenting votes are allowed, even if
kis increased by ties.)
kth largest are included. If false, a random selection of distances equal to the
kth is chosen to use exactly
knn3is essentially the same code as
knn3Trainis a copy of
knn. The underlying C code from the
classpacakge has been modifed 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)