equSA (version 1.1.5)

psical: An calculation of \(\psi\) scores.

Description

To compute an equvalent mearsure of partial correlation coeffients called \(\psi\) scores.

Usage

psical(iData,iMaxNei=as.integer(iDataNum/log(iDataNum)),
ALPHA1=0.05,GRID=2,iteration=100)

Arguments

iData

a nxp data matrix.

iMaxNei

Neiborhood size in correlation screening step, default to n/log(n).

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 \(\psi\)-partial correlation coefficient.

GRID

The number of components for the corrlation scores. The default value is 2.

iteration

Number of iterations for screening. The default value is 100.

Value

score

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.

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Details

This is the function to calculate \(\psi\) scores and can be used in combining or detecting difference of two networks.

References

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.

Examples

# NOT RUN {
# }
# NOT RUN {
library(equSA)
data(SR0)
subset <- SR0
psical(subset)
# }
# NOT RUN {
         
# }