Quality of estimated partial correlations

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Description

This function computed various performance measures of the estimated matrix of partial correlations.

Usage

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performance.pcor(inferred.pcor, true.pcor=NULL,
			fdr=TRUE, cutoff.ggm=0.8,verbose=FALSE,plot.it=FALSE)

Arguments

inferred.pcor

matrix of estimated partial correlations

true.pcor

true matrix of partial correlations. Default is true.pcor=NULL

fdr

logical. If fdr=TRUE, the entries of inferred.pcor are tested for significance. Default is fdr=TRUE

cutoff.ggm

default cutoff for significant partial correlations. Default is cutoff.ggm=0.8

verbose

Print information on test results etc.. Default is verbose=FALSE

plot.it

Plot test results and ROC-curves. Default is plot.it=FALSE

Details

This function computes a range of performance measures: The function always returns the number of selected edges, the binary matrix that encodes the edges, the connectivity and the percentage of positive correlations. If true.pcor is provided, the function also returns the power (= true positive rate), the false positive rate and the positive predictive value. For non-sparse estimates that involve testing (i.e. fdr=TRUE) the function also returns the area under the curve, and a pair of vectors of false and true positive rates. The latter can e.g. be used to plot a ROC-curve.

Value

num.selected

number of selected edges

adj

binary matrix that encodes the existence of an edge between two nodes.

connectivity

vector of length ncol(inferred.pcor). Its ith entry indicated the number of nodes that are connected to the ith node.

positive.cor

percentage of positive partial correlations out of all selected edges.

power

power (if true.pcor is provided)

ppv

positive predictive value (if true.pcor is provided)

tpr

true positive rate (=power) (if true.pcor is provided)

fpr

true positive rate (=power) (if true.pcor is provided)

auc

area under the curve (if true.pcor is provided and fdr=TRUE)

TPR

vector of true positive rates corresponding to varying cut-offs (if true.pcor is provided and fdr=TRUE)

FPR

vector of false positive rates corresponding to varying cut-offs (if true.pcor is provided and fdr=TRUE)

Author(s)

Juliane Schaefer, Nicole Kraemer

References

N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks using Gaussian Graphical Models", BMC Bioinformatics, 10:384

http://www.biomedcentral.com/1471-2105/10/384/