AFcorMI computes a predicted weighted mutual information adjacency matrix from a given correlation matrix.
a symmetric correlation matrix with values from -1 to 1.
number of observations from which the correlation was calcuated.
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
A matrix with the same size as the input correlation matrix, containing the predicted mutual information of
Steve Horvath, Lin Song, Peter Langfelder
#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))
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