To compute an equvalent mearsure of partial correlation coeffients called \(\psi\) scores.
psical(iData,iMaxNei,ALPHA1=0.05,GRID=2,iteration=100)a \(n\)x\(p\) data matrix.
Neiborhood size in correlation screening step, default to \(n/log(n)\), where \(n\) is the number of observation.
The significance level of correlation screening. In general, a high significance level of correlation screening will lead to a slightly large separator set \(S_{ij}\), which reduces the risk of missing some important variables in the conditioning set. Including a few false variables in the conditioning set will not hurt much the accuracy of the \(\psi\)-partial correlation coefficient.
The number of components for the corrlation scores. The default value is 2.
Number of iterations for screening. The default value is 100.
Estimated \(\psi\) score matrix which has 3 columns. The first two columns denote the pair indices of variables i and j and the last column denote the calculated \(\psi\) scores for this pair.
This is the function to calculate \(\psi\) scores and can be used in combining or detecting difference of two networks.
Liang, F., Song, Q. and Qiu, P. (2015). An Equivalent Measure of Partial Correlation Coefficients for High Dimensional Gaussian Graphical Models. J. Amer. Statist. Assoc., 110, 1248-1265.
Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961-977.
# NOT RUN {
library(equSA)
data <- GauSim(100,100)$data
psical(data)
# }
# NOT RUN {
# }
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