feature_network {mosbi} | R Documentation

## Generate a co-occurence network

### Description

The function computes a co-occurence network, based on the function `full_graph`. A similarity threshold is calculated using randomized biclusters (the bicluster size distribution is kept).

### Usage

    feature_network(
      bics,
      mat,
      n_randomizations = 5,
      n_steps = 100,
      plot_edge_dist = TRUE,
      sn_ratio = 1,
      error_threshold = 0.05,
      return_plot_data = FALSE,
      rr = 1,
      rc = 1,
      cc = 1,
      w = 0
    )

### Arguments

`bics` | A list of bicluster objects.
`mat` | The matrix used for biclustering.
`n_randomizations` | The number of randomizations for cut-off estimation. (The mean of all randomizations is used).
`n_steps` | Number of points where the difference between randomizations and the real data is evaluated.
`plot_edge_dist` | Show the plots for threshold estimation.
`sn_ratio` | If `TRUE`, the signal to noise ratio is computed, otherwise the `error_threshold` is used to to estimate the threshold at which only `error_threshold*100` percent of the edges are estimated to be random overlaps.
`error_threshold` | If `sn_ratio==FALSE` this cut-off is used to estimate the cut-off at which only `error_threshold*100` percent of the edges are estimated to be random overlaps.
`return_plot_data` | Please do not use outside of the package.
`rr` | See `full_graph`.
`rc` | See `full_graph`.
`cc` | See `full_graph`.
`w` | See parameter weighting of `full_graph`.

### Value

An object of class `cooccurrence_net`.

### Examples

    m <- matrix(seq(1:16), nrow=4)
    # m <- matrix(rnorm(10000), nrow=100)
    # bics <- c(run_fabia(m), run_isa(m), run_plaid(m))
    # feature_network(bics, m)

[Package mosbi version 1.0.3 Index]