kohonen (version 2.0.5)

xyf: Supervised version of Kohonen's self-organising maps

Description

Supervised version of self-organising maps for mapping high-dimensional spectra or patterns to 2D. The name stands for X-Y fused SOMs. One vector for each object is created by concatenating X and Y, and a SOM is trained in the usual way, with one exception: the distance of an object to a unit is the sum of separate distances for X and Y spaces. Prediction is done only using the X-space. For continuous Y, the Euclidean distance is used; for categorical Y the Tanimoto distance.

Usage

xyf(data, Y, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01),
    radius = quantile(nhbrdist, 0.67) * c(1, -1), xweight = 0.5,
    contin, toroidal = FALSE, n.hood, keep.data = TRUE)

Arguments

data

a matrix, with each row representing an object.

Y

property that is to be modelled. In case of classification, Y is a matrix of zeros, with exactly one '1' in each row indicating the class. For prediction of continuous properties, Y is a vector. A combination is possible, too, but one then sho

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 sma

xweight

the weight given to the X map in the calculation of distances for updating Y. Default is 0.5.

contin

parameter indicating whether Y is continuous or categorical. The default is to check whether all row sums of Y equal 1: in that case contin 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.

keep.data

save data in return value.

Value

• an object of class "kohonen" with components
• data
  data matrix, only returned if keep.data == TRUE.
• Y
  Y, only returned if keep.data == TRUE.
• contin
  parameter indicating whether Y is continuous or categorical.
• grid
  the grid, an object of class "somgrid".
• codes
  list of two matrices, containing codebook vectors for X and Y, respectively.
• changes
  matrix containing two columns of mean average deviations from code vectors. Column 1 contains deviations used for updating Y; column 2 for updating X.
• toroidal
  whether a toroidal map is used.
• 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.
• method
  the type of som, here "xyf".

References

W.J. Melssen, R. Wehrens, and L.M.C. Buydens. Chemom. Intell. Lab. Syst., 83, 99-113 (2006).

See Also

som, bdk, plot.kohonen, predict.kohonen

Examples

### Simulated example
library(MASS)

csize <- 15
c1 <- mvrnorm(csize, mu=c(5,3,4),
              Sigma=matrix(c(2, 0,0,0, 2, 1, 0, 1, 2),3,3))
c2 <- mvrnorm(csize, mu=c(5.5, 3.5, 4.5),
              Sigma=matrix(c(2, 0,0,0, 2, 1, 0, 1, 2),3,3))
c3 <- mvrnorm(csize, mu=c(0,0,0),
              Sigma=matrix(c(2, 0,0,0, 2, 1, 0, 1, 2),3,3))

X <- rbind(c1, c2, c3)
classes <- c(rep(1, csize), rep(2, csize), rep(3, csize))

xyfmap <- xyf(X, classvec2classmat(classes), somgrid(4, 4, "hexagonal"))
plot(xyfmap, "mapping", pch=rep(1:3, rep(15, 3)), col=rep(1:3, rep(15, 3)))

### Wine example
data(wines)
set.seed(7)

training <- sample(nrow(wines), 120)
Xtraining <- scale(wines[training,])
Xtest <- scale(wines[-training,],
               center = attr(Xtraining, "scaled:center"),
               scale = attr(Xtraining, "scaled:scale"))

xyf.wines <- xyf(Xtraining,
                 factor(wine.classes[training]),
                 grid = somgrid(5, 5, "hexagonal"))

xyf.prediction <- predict(xyf.wines, newdata=Xtest)
table(wine.classes[-training], xyf.prediction$prediction)