# population2sample.test.MinPv: Identify differences of partial correlations between two... In BrainCon: Inference the Partial Correlations Based on Time Series Data

## Description

Identify differences of partial correlations between two populations in two groups of time series data by controlling the exceedance rate of the false discovery proportion (FDP) at α=0.05. The method is based on the minimum of the p-values. Input two groups of data Z_1 and Z_2, each contains values of p interested variables of individuals (the number of individuals in two groups can be different) over n periods.

## Usage

 `1` ```population2sample.test.MinPv(popEst1, popEst2, alpha = 0.05, c0 = 0.1) ```

## Arguments

 `popEst1` A `popEst` class object. `popEst2` A `popEst` class object. `alpha` significance level, default value is `0.05`. `c0` threshold of the exceedance rate of the false discovery proportion (FDP), default value is `0.1`. The choice of `c0` depends on the empirical problem. A smaller value of `c0` will reduce false positives, but it may also cost more false negatives.

## Value

A p*p matrix with values 0 or 1. If the j-th row and k-th column of the matrix is 1, then the partial correlation coefficients between the j-th variable and the k-th variable in two populations are identified to be unequal.

## References

Genovese C., and Wasserman L. (2006). Exceedance Control of the False Discovery Proportion, Journal of the American Statistical Association, 101, 1408-1417

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 4 5 6``` ```## Quick example for the two-sample case inference data(popsimA) data(popsimB) pc1 = population.est(popsimA) pc2 = population.est(popsimB) Res = population2sample.test.MinPv(pc1, pc2) # conducting hypothesis test ```

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