class (version 7.3-14)

lvqinit: 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.

Value

A codebook, represented as a list with components `x` and `cl` giving the examples and classes.

Details

Selects `size` examples from the training set without replacement with proportions proportional to the prior or the original proportions.

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.

`lvq1`, `lvq2`, `lvq3`, `olvq1`, `lvqtest`

Examples

Run this code
``````# NOT RUN {
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)
# }
``````

Run the code above in your browser using DataCamp Workspace