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, `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.

### 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

    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)

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