Description Usage Arguments Details Value Author(s) Examples
$k$-nearest neighbour classification that can return class votes for all classes.
1 2 3 4 5 6 7 8 9 10 |
formula |
a formula of the form |
data |
optional data frame containing the variables in the model formula. |
subset |
optional vector specifying a subset of observations to be used. |
na.action |
function which indicates what should happen when
the data contain |
k |
number of neighbours considered. |
x |
a matrix of training set predictors |
y |
a factor vector of training set classes |
... |
additional parameters to pass to |
train |
matrix or data frame of training set cases. |
test |
matrix or data frame of test set cases. A vector will be interpreted as a row vector for a single case. |
cl |
factor of true classifications of training set |
l |
minimum vote for definite decision, otherwise |
prob |
If this is true, the proportion of the votes for each class
are returned as attribute |
use.all |
controls handling of ties. If true, all distances equal to the |
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).
An object of class knn3
. See predict.knn3
.
knn
by W. N. Venables and B. D. Ripley and
ipredknn
by
Torsten.Hothorn <Torsten.Hothorn@rzmail.uni-erlangen.de>,
modifications by Max Kuhn and Andre Williams
1 2 3 4 5 6 7 8 9 | 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)
|
Loading required package: lattice
Loading required package: ggplot2
[1] "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s" "s"
[20] "s" "s" "s" "s" "s" "s" "c" "c" "v" "c" "c" "c" "c" "c" "v" "c" "c" "c" "c"
[39] "c" "c" "c" "c" "c" "c" "c" "c" "c" "c" "c" "c" "v" "c" "c" "v" "v" "v" "v"
[58] "v" "c" "v" "v" "v" "v" "c" "v" "v" "v" "v" "v" "v" "v" "v" "v" "v" "v"
attr(,"prob")
c s v
[1,] 0.0000000 1 0.0000000
[2,] 0.0000000 1 0.0000000
[3,] 0.0000000 1 0.0000000
[4,] 0.0000000 1 0.0000000
[5,] 0.0000000 1 0.0000000
[6,] 0.0000000 1 0.0000000
[7,] 0.0000000 1 0.0000000
[8,] 0.0000000 1 0.0000000
[9,] 0.0000000 1 0.0000000
[10,] 0.0000000 1 0.0000000
[11,] 0.0000000 1 0.0000000
[12,] 0.0000000 1 0.0000000
[13,] 0.0000000 1 0.0000000
[14,] 0.0000000 1 0.0000000
[15,] 0.0000000 1 0.0000000
[16,] 0.0000000 1 0.0000000
[17,] 0.0000000 1 0.0000000
[18,] 0.0000000 1 0.0000000
[19,] 0.0000000 1 0.0000000
[20,] 0.0000000 1 0.0000000
[21,] 0.0000000 1 0.0000000
[22,] 0.0000000 1 0.0000000
[23,] 0.0000000 1 0.0000000
[24,] 0.0000000 1 0.0000000
[25,] 0.0000000 1 0.0000000
[26,] 1.0000000 0 0.0000000
[27,] 1.0000000 0 0.0000000
[28,] 0.4000000 0 0.6000000
[29,] 1.0000000 0 0.0000000
[30,] 1.0000000 0 0.0000000
[31,] 1.0000000 0 0.0000000
[32,] 1.0000000 0 0.0000000
[33,] 1.0000000 0 0.0000000
[34,] 0.4000000 0 0.6000000
[35,] 0.8000000 0 0.2000000
[36,] 1.0000000 0 0.0000000
[37,] 1.0000000 0 0.0000000
[38,] 1.0000000 0 0.0000000
[39,] 1.0000000 0 0.0000000
[40,] 1.0000000 0 0.0000000
[41,] 1.0000000 0 0.0000000
[42,] 1.0000000 0 0.0000000
[43,] 1.0000000 0 0.0000000
[44,] 1.0000000 0 0.0000000
[45,] 1.0000000 0 0.0000000
[46,] 1.0000000 0 0.0000000
[47,] 1.0000000 0 0.0000000
[48,] 1.0000000 0 0.0000000
[49,] 1.0000000 0 0.0000000
[50,] 1.0000000 0 0.0000000
[51,] 0.0000000 0 1.0000000
[52,] 0.8000000 0 0.2000000
[53,] 0.6000000 0 0.4000000
[54,] 0.0000000 0 1.0000000
[55,] 0.0000000 0 1.0000000
[56,] 0.0000000 0 1.0000000
[57,] 0.0000000 0 1.0000000
[58,] 0.0000000 0 1.0000000
[59,] 0.6666667 0 0.3333333
[60,] 0.2000000 0 0.8000000
[61,] 0.0000000 0 1.0000000
[62,] 0.0000000 0 1.0000000
[63,] 0.0000000 0 1.0000000
[64,] 0.6000000 0 0.4000000
[65,] 0.0000000 0 1.0000000
[66,] 0.0000000 0 1.0000000
[67,] 0.0000000 0 1.0000000
[68,] 0.0000000 0 1.0000000
[69,] 0.0000000 0 1.0000000
[70,] 0.0000000 0 1.0000000
[71,] 0.0000000 0 1.0000000
[72,] 0.2000000 0 0.8000000
[73,] 0.0000000 0 1.0000000
[74,] 0.0000000 0 1.0000000
[75,] 0.2000000 0 0.8000000
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