Infer networks from Gaussian data by \(\psi\)-learning algorithm.
equSAR(iData,iMaxNei,ALPHA1=0.05,ALPHA2=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 significance level of \(\psi\) screening.
The number of components for the \(\psi\) scores. The default value is 2.
Number of iterations for screening. The default value is 100.
A list of two elements:
\(p\)x\(p\) adjacency matrix of the generated graph.
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 main function of the package that fit the Gaussian Graphical Models and obtain the \(\psi\) scores and adjacency matrix.
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
equSAR(data)
# }
# NOT RUN {
# }
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