Description Usage Arguments Details Value Author(s) References Examples
Infer networks from Gaussian data by ψlearning algorithm.
1  equSAR(iData,iMaxNei,ALPHA1=0.05,ALPHA2=0.05,GRID=2,iteration=100)

iData 
a nxp data matrix. 
iMaxNei 
Neiborhood size in correlation screening step, default to n/log(n), where n is the number of observation. 
ALPHA1 
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 ψpartial correlation coefficient. 
ALPHA2 
The significance level of ψ screening. 
GRID 
The number of components for the ψ scores. The default value is 2. 
iteration 
Number of iterations for screening. The default value is 100. 
This is the main function of the package that fit the Gaussian Graphical Models and obtain the ψ scores and adjacency matrix.
A list of two elements:
Adj 
pxp adjacency matrix of the generated graph. 
score 
Estimated ψ 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 ψ scores for this pair. 
Bochao Jia and Faming Liang
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, 12481265.
Liang, F. and Zhang, J. (2008) Estimating FDR under general dependence using stochastic approximation. Biometrika, 95(4), 961977.
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