# 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

 `1` ```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

 ```1 2 3``` ```## 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.