supersom: Super-organising maps

Description

An extension of xyf maps to multiple data layers, possibly with different numbers of variables (though equal numbers of objects). NAs are allowed (see below). A weighted distance over all layers is calculated to determine the winning units during training.

Usage

supersom(data, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01), radius = quantile(nhbrdist, 0.67) * c(1, -1),  contin, toroidal = FALSE, n.hood, whatmap = NULL, weights = 1, maxNA.fraction = .5, keep.data = TRUE)

Arguments

data

list of data matrices.

grid

a grid for the representatives: see somgrid.

rlen

the number of times the complete data set will be presented to the network.

alpha

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.

radius

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 run from the given number to the negative value of that number; as soon as the neighbourhood gets smaller than one only the winning unit will be updated. The default is to start with a value that covers 2/3 of all unit-to-unit distances.

contin

parameter indicating whether data are continuous or categorical, i.e. a logical vector. If only TRUE or FALSE is given, this is taken to hold for all elements in data. The default is to check whether row sums in the data matrices are equal to 1: in that case the corresponding contin element is FALSE.

toroidal

if TRUE, the edges of the map are joined. Note that in a hexagonal toroidal map, the number of rows must be even.

n.hood

the shape of the neighbourhood, either "circular" or "square". The latter is the default for rectangular maps, the former for hexagonal maps.

whatmap

For supersom maps: what layers to use in the mapping.

weights

the weights given to individual layers. Default is 1/n, with n the number of layers.

maxNA.fraction

the maximal fraction of values that may be NA to prevent the row or column to be removed.

keep.data

save data in return value.

Value

data: data matrix, only returned if keep.data == TRUE.
contin: parameter indicating whether elements of data are continuous or categorical.
na.rows: indices of objects (rows) that are removed because at least one of the layers has to many NAs for these objects.
unit.classif: winning units for all data objects, only returned if keep.data == TRUE.
distances: distances of objects to their corresponding winning unit, only returned if keep.data == TRUE.
grid: the grid, an object of class somgrid.
codes: a list of matrices containing codebook vectors.
changes: matrix of mean average deviations from code vectors; every map corresponds with one column.
toroidal: whether a toroidal map is used.
n.hood: the shape of the neighbourhood, either "circular" or "square". The latter is the default for rectangular maps, the former for hexagonal maps.
weights: For supersom maps: weights of layers uses in the mapping.
whatmap: For supersom maps: what layers to use in the mapping.
method: type of map, here "supersom".

References

R. Wehrens and L.M.C. Buydens, J. Stat. Softw. 21 (5), 2007

Examples

Run this code

data(yeast)
yeast.supersom <- supersom(yeast, somgrid(6, 6, "hexagonal"), whatmap = 3:6)
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", keepMargins = TRUE)