wccxyf: Supervised mapping of spectra with self-organising maps
In wccsom: SOM Networks for Comparing Patterns with Peak Shifts

Description Usage Arguments Value Author(s) References See Also Examples

Supervised self-organising maps for mapping high-dimensional spectra or patterns to 2D; instead of Euclidean distance, the weighted cross correlation (WCC) similarity measure is used. Modelled after the SOM function in package 'class'. wccxyf takes 'continous' patterns, i.e. datapoints are equidistant.

At this point, no facilities are implemented for growing networks or k-means-like fine-tuning of the maps, such as in function wccsom.

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wccxyf(data, Y, grid=somgrid(), rlen = 100, alpha = c(0.05, 0.01),
       radius = quantile(nhbrdist, 0.67), xweight = 0.5, trwidth = 20,
       toroidal = FALSE, keep.data = TRUE)

`data`	Spectra or patterns to be mapped: a matrix, with each row representing a compound.
`Y`	Property for each pattern, either a numerical vector or matrix, or a class matrix. In the latter case, the Tanimoto distance is used for Y; in all other cases (also for combinations of numerical and class properties) the Euclidean distance is used.
`grid`	A grid for the representatives: see 'somgrid'.
`rlen`	the number of times the complete data set will be presented to the network.
`alpha`	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 initial radius of the neighbourhood to be used for each update: the decrease is exponential over `rlen` updates in such a way that after one-third of the updates only the winning unit is updated. The default is to start with a value that covers 2/3 of all units.
`xweight`	weight of X matrix in determining the distances of objects to units.
`trwidth`	width of the triangle function used in the WCC measure, given in the number of data points.
`toroidal`	if TRUE, then the edges of the map are joined. Note that in a toroidal hexagonal map, the number of rows must be even.
`keep.data`	store training data and their mapping in the network.

an object of class '"wccsom"' with components

`grid`	the grid, an object of class '"somgrid"'.
`changes`	vector of mean average deviations from code vectors
`codes`	a matrix of code vectors.
`trwdth`	the triangle width used for the WCC measure
`acors`	autocorrelations of the code vectors.
`toroidal`	setting of parameter 'toroidal'.
`FineTune`	setting of parameter 'FineTune'.
`unit.classif`	mapping of training data: a vector of unit numbers. Only if `keep.data` equals TRUE.
`wccs`	WCC values of all training data, compared to the best matching codebook vector. Only if `keep.data` equals TRUE.
`data.acors`	WAC values for training data. Only if `keep.data` equals TRUE.

Ron Wehrens

FIXME: this page is a copy of wccsom, should be edited further

SOM, plot.wccsom, wccsom, wcc

## Not run: 
data(degelder)
gr <- somgrid(5, 5, "hexagonal")
set.seed(7)
x <- wccxyf(degelder$patterns, degelder$properties[,"cell.vol"],
            grid=gr, trwidth=20, rlen=100)

## End(Not run)