| map.kohonen {kohonen} | R Documentation |
Map a data matrix onto a trained SOM.
map.kohonen(x, newdata, whatmap = NULL, weights,
scale.distances = (nmaps > 1), ...)
x |
A trained supervised or unsupervised SOM obtained from
functions som, xyf, or bdk. |
newdata |
Data matrix, with rows corresponding to objects. |
whatmap |
For supersom maps: the layers to take into
account. |
weights |
For supersom maps: weights of the layers that
are used for mapping. |
scale.distances |
whether to rescale distances per layer in the
case of supersom maps (default): if TRUE the maximal
distance of each layer equals one. If the absolute values of the
distances per layer should be used, this argument should be set to
FALSE. Note that in that case, when mapping the training
data, the result returned by map.kohonen will differ from the
mapping present in the map. |
... |
Currently ignored. |
A list with elements
unit.classif |
a vector of units that are closest to the objects in the data matrix. |
dists |
distances (currently only Euclidean distances) of the objects to the units. |
whatmap,weights,scale.distances |
Values used for these arguments. |
Ron Wehrens
data(wines)
set.seed(7)
training <- sample(nrow(wines), 120)
Xtraining <- scale(wines[training, ])
somnet <- som(Xtraining, somgrid(5, 5, "hexagonal"))
mapping <- map(somnet,
scale(wines[-training, ],
center=attr(Xtraining, "scaled:center"),
scale=attr(Xtraining, "scaled:scale")))