flood: The Flood Algorithm
In restlos: Robust Estimation of Location and Scatter

Description Usage Arguments Details Value Author(s) References Examples

The function determines a robust subsample utilizing self-organizing maps (SOM).

1	flood(data, Nx=10, Ny=10, rlen=2000)

`data`	At least a two-dimensional data matrix is required. Number of observations needs to be greater than number of dimensions.
`Nx`	Size of the SOM-net in x direction. Default is 10.
`Ny`	Size of the SOM-net in y direction. Default is 10.
`rlen`	Number of iterations during SOM learn process. Default is 2000.

The function first calls the som function within the som-package. The results are subsequently used to determine a robust subsample. Arguments Nx, Ny and rlen are passed to som. These arguments should be selected depending on the size of the data set (number of observations/dimensions). The larger the data set the larger the net size and the number of iterations should be. Note: At the moment only rectangular and quadratic SOM nets are supported.

`som.results`	SOM results as delivered by `som`.
`som.neigh`	A matrix showing for every neuron (first column) the index off the neighboring neurons (columns 2-5).
`umatrix`	The U-matrix shows the U-value for every neuron.
`winneuron`	Vector of length `n` giving the index of the nearest neuron (Euclidean distance).
`lib`	List of all basins found. Index of neurons. Smallest subsample of size `(n+d+1)/2`.
`lin`	List of all neighboring neurons per basin. Index of neurons. Smallest subsample of size `(n+d+1)/2`.
`geb`	Number of associated data points per basin. Smallest subsample of size `(n+d+1)/2`.
`l`	Internal value necessary for plotting.
`fafh`	Data for plotting the flood area flood height curve.
`fafh.lib`	Internal data necessary for plotting extented flooding.
`fafh.drin`	Internal data necessary for plotting extented flooding.
`drin`	Robust subsample of minimal size.

Steffen Liebscher <steffen.liebscher@wiwi.uni-halle.de>

Liebscher, S., Kirschstein, T., and Becker, C. (2012): The Flood Algorithm - A Multivariate, Self-Organizing-Map-Based, Robust Location and Covariance Estimator, Statistics and Computing, 22(1), 325-336, DOI: 10.1007/s11222-011-9250-3.