population.test.MinPv: The one-sample population inference using Genovese and...

Description Usage Arguments Value References Examples

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

Description

Identify the nonzero partial correlations in one-sample population, based on 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 data Z , contains values of p interested variables.

Usage

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population.test.MinPv(popEst, alpha = 0.05, c0 = 0.1)

Arguments

popEst

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.

Value

A p*p matrix with values 0 or 1.

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 one-sample population inference
data(popsimA)
pc = population.est(popsimA)            # estimating partial correlation coefficients
Res  = population.test.MinPv(pc)        # conducting hypothesis test

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