| lvqinit {class} | R Documentation |
Construct an initial codebook for LVQ methods.
lvqinit(x, cl, size, prior, k = 5)
x |
a matrix or data frame of training examples, n by p.
|
cl |
the classifications for the training examples. A vector or factor of
length n.
|
size |
the size of the codebook. Defaults to min(round(0.4*ng*(ng-1 + p/2),0), n)
where ng is the number of classes.
|
prior |
Probabilities to represent classes in the codebook. Default proportions in the training set. |
k |
k used for k-NN test of correct classification. Default is 5. |
Selects size examples from the training set without replacement with
proportions proportional to the prior or the original proportions.
A codebook, represented as a list with components x and cl giving
the examples and classes.
Kohonen, T. (1990) The self-organizing map. Proc. IEEE 78, 1464–1480.
Kohonen, T. (1995) Self-Organizing Maps. Springer, Berlin.
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.
lvq1, lvq2, lvq3, olvq1, lvqtest
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)))
cd <- lvqinit(train, cl, 10)
lvqtest(cd, train)
cd1 <- olvq1(train, cl, cd)
lvqtest(cd1, train)