individual.est: Estimate individual-level partial correlation coefficients

In BrainCon: Inference the Partial Correlations Based on Time Series Data

Description

Estimate individual-level partial correlation coefficients in time series data with 1-α confidence interval. It's not a joint confidence interval for multiple tests.

Usage

individual.est(X, alpha = 0.05, lambda = NULL, ci = TRUE)

Arguments

X	time series data of an individual which is a n*p numeric matrix.
alpha	significance level, default value is 0.05.
lambda	a penalty parameter used in lasso of order sqrt(log(p)/n), if NULL, 2*sqrt(log(p)/n) will be used.
ci	a logical indicating whether to compute 1-α confidence interval, default value is TRUE.

Value

An indEst class object containing two or four components.

coef a p*p partial correlation coefficients matrix.

ci.lower a p*p numeric matrix containing the lower bound of 1-α confidence interval, returned if ci is TRUE.

ci.upper a p*p numeric matrix containing the upper bound of 1-α confidence interval, returned if ci is TRUE.

asym.ex a matrix measuring the asymptotical expansion of estimates, which will be used for multiple tests.

References

Qiu Y. and Zhou X. (2021). Inference on multi-level partial correlations based on multi-subject time series data, Journal of the American Statistical Association, 00, 1-15

Examples

## Quick example for the individual-level estimates
data(indsim)
pc = individual.est(indsim)       # estimating partial correlation coefficients

BrainCon documentation built on Sept. 30, 2021, 5:10 p.m.