Kohonen's Self-Organizing Maps are a crude form of multidimensional scaling.

`batchSOM(data, grid = somgrid(), radii, init)`

data

a matrix or data frame of observations, scaled so that Euclidean distance is appropriate.

grid

A grid for the representatives: see `somgrid`

.

radii

the radii of the neighbourhood to be used for each pass: one pass is
run for each element of `radii`

.

init

the initial representatives. If missing, chosen (without replacement)
randomly from `data`

.

An object of class `"SOM"`

with components

the grid, an object of class `"somgrid"`

.

a matrix of representatives.

The batch SOM algorithm of Kohonen(1995, section 3.14) is used.

Kohonen, T. (1995) *Self-Organizing Maps.* Springer-Verlag.

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.

# NOT RUN { require(graphics) data(crabs, package = "MASS") lcrabs <- log(crabs[, 4:8]) crabs.grp <- factor(c("B", "b", "O", "o")[rep(1:4, rep(50,4))]) gr <- somgrid(topo = "hexagonal") crabs.som <- batchSOM(lcrabs, gr, c(4, 4, 2, 2, 1, 1, 1, 0, 0)) plot(crabs.som) bins <- as.numeric(knn1(crabs.som$code, lcrabs, 0:47)) plot(crabs.som$grid, type = "n") symbols(crabs.som$grid$pts[, 1], crabs.som$grid$pts[, 2], circles = rep(0.4, 48), inches = FALSE, add = TRUE) text(crabs.som$grid$pts[bins, ] + rnorm(400, 0, 0.1), as.character(crabs.grp)) # }