a matrix or data frame of observations, scaled so that Euclidean
distance is appropriate.
grid
A grid for the representatives: see somgrid.
rlen
the number of updates: used only in the defaults for alpha and radii.
alpha
the amount of change: one update is done for each element of alpha.
Default is to decline linearly from 0.05 to 0 over rlen updates.
radii
the radii of the neighbourhood to be used for each update: must be the
same length as alpha. Default is to decline linearly from 4 to 1
over rlen updates.
init
the initial representatives. If missing, chosen (without replacement)
randomly from data.
Details
alpha and radii can also be lists, in which case each component is
used in turn, allowing two- or more phase training.
Value
An object of class "SOM" with components
grid
the grid, an object of class "somgrid".
codes
a matrix of representatives.
References
Kohonen, T. (1995) Self-Organizing Maps. Springer-Verlag
Kohonen, T., Hynninen, J., Kangas, J. and Laaksonen, J. (1996)
SOM PAK: The self-organizing map program package.
Laboratory of Computer and Information Science, Helsinki University
of Technology, Technical Report A31.
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
somgrid, batchSOM
Examples
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 <- SOM(lcrabs, gr)
plot(crabs.som)
## 2-phase training
crabs.som2 <- SOM(lcrabs, gr,
alpha = list(seq(0.05, 0, len = 1e4), seq(0.02, 0, len = 1e5)),
radii = list(seq(8, 1, len = 1e4), seq(4, 1, len = 1e5)))
plot(crabs.som2)