get_fit.cor {GWENA} | R Documentation

## Calculating best fit of a power low on correlation matrix computed on expression data

### Description

Adjust a correlation matrix depending of the type of network, then try to parameter a power law for best fit

### Usage

    get_fit.cor(
      cor_mat,
      fit_cut_off = 0.9,
      network_type = c("unsigned", "signed", "signed hybrid"),
      block_size = NULL,
      ...
    )

### Arguments

`cor_mat` | matrix or data.frame of genes correlation.
`fit_cut_off` | float, cut off by which R^2 (coefficient of determination) will be thresholded. Must be in ]0;1[.
`network_type` | string giving type of network to be used. Either "unsigned", "signed", "signed hybrid". See details.
`block_size` | integer giving size of blocks by which operations can be proceed. Helping if working with low capacity computers. If null, will be estimated.
`...` | any other parameter compatible with `pickSoftThreshold.fromSimilarity`

### Details

network_type indicate which transformation will be applied on the correlation matrix to return the similarity score.

signed

  will modify the range [-1;1] to [0.5;1.5] (because of log10 beeing used for scale free index computation)

unsigned

  will return absolute value (moving from [-1;1] to [0;1])

signed hybrid

  will replace all negative values by 0 (moving from [-1;1] to [0;1])

### Value

A list containing power of the law for best fit, fit table, and metadata about the arguments used.

### Examples

    get_fit.cor(cor_mat = cor(kuehne_expr[, seq_len(100)]))

[Package GWENA version 1.0.1 Index]