runSOM {CytoTree} | R Documentation

## calculation SOM in CYT object

### Description

Build a self-organizing map

### Usage

    runSOM(
      object,
      xdim = 6,
      ydim = 6,
      rlen = 8,
      mst = 1,
      alpha = c(0.05, 0.01),
      radius = 1,
      init = FALSE,
      distf = 2,
      codes = NULL,
      importance = NULL,
      method = "euclidean",
      verbose = FALSE,
      ...
    )

### Arguments

`object` | an CYT object
`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
`distf` | Distance function (1=manhattan, 2=euclidean, 3=chebyshev, 4=cosine)
`codes` | Cluster centers to start with
`importance` | array with numeric values. Parameters will be scaled according to importance
`method` | the distance measure to be used. This must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given. See `dist`
`verbose` | logical. Whether to print calculation progress.
`...` | Parameters passing to `SOM` function

### Value

an CYT object with som.id in CYT object

### References

This code is strongly based on the `SOM` function. Which is developed by Sofie Van Gassen, Britt Callebaut and Yvan Saeys (2018).

### See Also

`BuildSOM`

`SOM`

### Examples

    cyt.file <- system.file("extdata/cyt.rds", package = "CytoTree")
    cyt <- readRDS(file = cyt.file)

    cyt <- runSOM(cyt, xdim = 10, ydim = 10, verbose = TRUE)

[Package CytoTree version 1.4.0 Index]