edgeWeight {PANR} | R Documentation

## Compute edge weights for posterior association networks

### Description

This is an internal function to compute edge weights before inferring a posterior association network.

### Usage

    edgeWeight(object, which="bm1", type="SNR", log=TRUE, ...)

### Arguments

`object` | an object of S4 class `PAN`.
`which` | a character value specifying which BetaMixture modelling result to use: first-order (if 'bm1') or second-order (if 'bm2').
`type` | a character value giving the type of edge weight to compute: signal- to-noise ratio (if 'SNR'), posterior probability odd (if 'PPR') or posterior probability (if 'PP').
`log` | a logical value specifying whether or not to compute logrithms for edge weights.

### Details

This function will be called by `infer` to compute edge weights for posterior association networks. When inferring a signed PAN, signal-to-noise ratios are suggested to use; while inferring a PAN of only positive associations, posterior probability odds or posterior probabilities are preferred.

### Value

This function will return a numeric adjacency matrix of edge weights.

### Author(s)

Xin Wang xw264@cam.ac.uk

### References

Xin Wang, Mauro Castro, Klaas W. Mulder and Florian Markowetz, Posterior association networks and enriched functional gene modules inferred from rich phenotypic perturbation screens, in preparation.

### See Also

`infer`

[Package PANR version 1.26.0 Index]