AFcorMI: Prediction of Weighted Mutual Information Adjacency Matrix by...

In WGCNA: Weighted Correlation Network Analysis

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

See Also

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 Sept. 18, 2024, 5:08 p.m.