SOM {class}  R Documentation 
Kohonen's SelfOrganizing Maps are a crude form of multidimensional scaling.
SOM(data, grid = somgrid(), rlen = 10000, alpha, 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 .

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 .

alpha
and radii
can also be lists, in which case each component is
used in turn, allowing two or more phase training.
an object of class "SOM"
with components
grid 
the grid, an object of class "somgrid" .

codes 
a matrix of representatives. 
Kohonen, T. (1995) SelfOrganizing Maps. SpringerVerlag
Kohonen, T., Hynninen, J., Kangas, J. and Laaksonen, J. (1996) SOM PAK: The selforganizing 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.
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) ## 2phase 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)