A supersom is an extension of self-organising maps (SOMs) to multiple
data layers, possibly with different numbers and different types of
variables (though equal numbers of objects). NAs are allowed. A
weighted distance over all layers is calculated to determine the
winning units during training.
Functions som and xyf are simply wrappers for supersoms
with one and two layers, respectively. Function nunits is a
utility function returning the number of units in the map.
som(X, ...)
xyf(X, Y, ...)
supersom(data, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01),
radius = quantile(nhbrdist, 2/3),
whatmap = NULL, user.weights = 1, maxNA.fraction = 0L,
keep.data = TRUE, dist.fcts = NULL,
mode = c("online", "batch", "pbatch"), cores = -1, init,
normalizeDataLayers = TRUE)
nunits(kohobj)An object of class "kohonen" with components
data matrix, only returned if keep.data == TRUE.
winning units for all data objects,
only returned if keep.data == TRUE.
distances of objects to their corresponding winning
unit, only returned if keep.data == TRUE.
the grid, an object of class somgrid.
a list of matrices containing codebook vectors.
matrix of mean average deviations from code vectors; every map corresponds with one column.
vector of row numbers with too many NA values
(according to argument maxNA.fraction.
input arguments presented to the function.
if normalizeDataLayers weights to
equalize the influence of the individual data layers, else a vector
of ones.
distance functions corresponding to all layers of the data, not just the ones indicated by the whatmap argument.
numerical data matrices, or factors. No data.frame
objects are allowed - convert them to matrices first.
list of data matrices (numerical) of factors. If a vector
is entered, it will be converted to a one-column matrix. No
data.frame objectss are allowed.
a grid for the codebook vectors:
see somgrid.
the number of times the complete data set will be presented to the network.
learning rate, a vector of two numbers indicating the
amount of change. Default is to decline linearly from 0.05 to 0.01
over rlen updates. Not used for the batch algorithm.
the radius of the neighbourhood, either given as a
single number or a vector (start, stop). If it is given as a single
number the radius will change linearly from radius to zero; as
soon as the neighbourhood gets smaller than one only the winning unit
will be updated. Note that the default before version 3.0 was to run
from radius to -radius. If nothing is supplied, the
default is to start with a value that covers 2/3 of all unit-to-unit
distances.
What data layers to use. If unspecified all layers are used.
the weights given to individual layers. This can
be a single number (all layers have the same weight, the default), a
vector of the same length as the whatmap argument, or a vector
of the same length as the data argument. In xyf maps, this
argument provides the same functionality as the now-deprecated
xweight argument that was used prior to version 3.0.
the maximal fraction of values that may be NA to prevent the row to be removed.
if TRUE, return original data and mapping information. If FALSE, only return the trained map (in essence the codebook vectors).
vector of distance functions to be used for the
individual data layers, of the same length as the data
argument, or the same length of the whatmap argument. If the
length of this vector is one, the
same distance will be used for all layers. Admissable values
currently are "sumofsquares", "euclidean", "manhattan", and
"tanimoto". Default is to use "sumofsquares" for continuous data,
and "tanimoto" for factors.
type of learning algorithm.
number of cores to use in the "pbatch" learning mode. The default, -1, corresponds to using all available cores.
list of matrices, initial values for the codebook vectors. The list should have the same length as the data list, and corresponding numbers of variables (columns). Each list element should have a number of rows corresponding to the number of units in the map.
boolean, indicating whether
distance.weights should be calculated (see details section).
If normalizeDataLayers == FALSE the user weights
are applied to the data immediately.
an object of class kohonen.
Further arguments for the supersom function
presented to the som or xyf wrappers.
Ron Wehrens and Johannes Kruisselbrink
In order to avoid some layers to overwhelm others, simply
because of the scale of the data points, the supersom function
by default applies internal weights to balance this. The user.weights
argument is applied on top of that: the result is that when a user
specifies equal weights for all layers (the default), all layers
contribute equally to the global distance measure. For large data
sets (defined as containing more than 500 records), a sample of size
500 is used to calculate the mean distances in each data layer. If
normalizeDataLayers == FALSE the user weights are applied
directly to the data (distance.weights are set to 1).
Various definitions of the Tanimoto distance exist in the literature. The implementation here returns (for two binary vectors of length n) the fraction of cases in which the two vectors disagree. This is basically the Hamming distance divided by n - the incorrect naming is retained (for the moment) to guarantee backwards compatibility. If the vectors are not binary, they will be converted to binary strings (with 0.5 as the class boundary). This measure should not be used when variables are outside the range [0-1]; a check is done to make sure this is the case.
R. Wehrens and L.M.C. Buydens, J. Stat. Softw. 21 (5), 2007; R. Wehrens and J. Kruisselbrink, submitted, 2017.
somgrid, plot.kohonen,
predict.kohonen, map.kohonen
data(wines)
## som
som.wines <- som(scale(wines), grid = somgrid(5, 5, "hexagonal"))
summary(som.wines)
nunits(som.wines)
## xyf
xyf.wines <- xyf(scale(wines), vintages, grid = somgrid(5, 5, "hexagonal"))
summary(xyf.wines)
## supersom example
data(yeast)
yeast.supersom <- supersom(yeast, somgrid(6, 6, "hexagonal"),
whatmap = c("alpha", "cdc15", "cdc28", "elu"),
maxNA.fraction = .5)
plot(yeast.supersom, "changes")
obj.classes <- as.integer(yeast$class)
colors <- c("yellow", "green", "blue", "red", "orange")
plot(yeast.supersom, type = "mapping", col = colors[obj.classes],
pch = obj.classes, main = "yeast data")
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