# AFcorMI: Prediction of Weighted Mutual Information Adjacency Matrix by... In WGCNA: Weighted Correlation Network Analysis

 AFcorMI R Documentation

## Prediction of Weighted Mutual Information Adjacency Matrix by Correlation

### Description

AFcorMI computes a predicted weighted mutual information adjacency matrix from a given correlation matrix.

### Usage

```AFcorMI(r, m)
```

### Arguments

 `r` a symmetric correlation matrix with values from -1 to 1. `m` number of observations from which the correlation was calcuated.

### Details

This function is a one-to-one prediction when we consider correlation as unsigned. The prediction corresponds to the `AdjacencyUniversalVersion2` discussed in the help file for the function `mutualInfoAdjacency`. For more information about the generation and features of the predicted mutual information adjacency, please refer to the function `mutualInfoAdjacency`.

### Value

A matrix with the same size as the input correlation matrix, containing the predicted mutual information of type `AdjacencyUniversalVersion2`.

### Author(s)

Steve Horvath, Lin Song, Peter Langfelder

`mutualInfoAdjacency`

### Examples

```#Simulate a data frame datE which contains 5 columns and 50 observations
m=50
x1=rnorm(m)
r=.5; x2=r*x1+sqrt(1-r^2)*rnorm(m)
r=.3; x3=r*(x1-.5)^2+sqrt(1-r^2)*rnorm(m)
x4=rnorm(m)
r=.3; x5=r*x4+sqrt(1-r^2)*rnorm(m)
datE=data.frame(x1,x2,x3,x4,x5)
#calculate predicted AUV2
cor.data=cor(datE, use="p")
AUV2=AFcorMI(r=cor.data, m=nrow(datE))

```

WGCNA documentation built on Jan. 22, 2023, 1:34 a.m.