population2sample.test.MinPv: Identify differences of partial correlations between two...

Description Usage Arguments Value References Examples

View source: R/population2sample.test.MinPv.R

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

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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

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## 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.