psical: A calculation of psi scores.

In equSA: Learning High-Dimensional Graphical Models

Description

To compute an equvalent mearsure of partial correlation coeffients called ψ scores.

Usage

psical(iData,iMaxNei,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), 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 ψ-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.

Details

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

Value

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.

Author(s)

Bochao Jia, Faming [e-mail]

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

library(equSA)
data <- GauSim(100,100)$data
psical(data)

         

equSA documentation built on May 6, 2019, 1:06 a.m.