performance.pcor: Quality of estimated partial correlations

In parcor: Regularized estimation of partial correlation matrices

Description

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

Usage

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/

parcor documentation built on May 1, 2019, 9:10 p.m.