| lvqinit {class} | R Documentation |
Initialize a LVQ Codebook
Description
Construct an initial codebook for LVQ methods.
Usage
lvqinit(x, cl, size, prior, k = 5)
Arguments
x |
a matrix or data frame of training examples, |
cl |
the classifications for the training examples. A vector or factor of
length |
size |
the size of the codebook. Defaults to |
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. |
Details
Selects size examples from the training set without replacement with
proportions proportional to the prior or the original proportions.
Value
A codebook, represented as a list with components x and cl giving
the examples and classes.
References
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.
See Also
lvq1, lvq2, lvq3, olvq1, lvqtest
Examples
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)