SOM: Build a self-organizing map

In FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data

Description

Build a self-organizing map

Usage

SOM(
  data,
  xdim = 10,
  ydim = 10,
  rlen = 10,
  mst = 1,
  alpha = c(0.05, 0.01),
  radius = stats::quantile(nhbrdist, 0.67) * c(1, 0),
  init = FALSE,
  initf = Initialize_KWSP,
  distf = 2,
  silent = FALSE,
  codes = NULL,
  importance = NULL
)

Arguments

data	Matrix containing the training data
xdim	Width of the grid
ydim	Hight of the grid
rlen	Number of times to loop over the training data for each MST
mst	Number of times to build an MST
alpha	Start and end learning rate
radius	Start and end radius
init	Initialize cluster centers in a non-random way
initf	Use the given initialization function if init==T (default: Initialize_KWSP)
distf	Distance function (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)
silent	If FALSE, print status updates
codes	Cluster centers to start with
importance	array with numeric values. Parameters will be scaled according to importance

Value

A list containing all parameter settings and results

References

This code is strongly based on the kohonen package. R. Wehrens and L.M.C. Buydens, Self- and Super-organising Maps in R: the kohonen package J. Stat. Softw., 21(5), 2007

See Also

BuildSOM

FlowSOM documentation built on Nov. 8, 2020, 6:40 p.m.