population.test: The one-sample population inference

Description Usage Arguments Value References See Also Examples

View source: R/population.test.R

Description

Identify the nonzero partial correlations in one-sample population, based on false discovery proportion controlling. at α=0.05, considering time dependence. Input data Z , contains values of p interested variables.

Usage

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population.test(popEst, alpha = 0.05, c0 = 0.1, MBT = 3000)

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. 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.

MBT

times of multiplier bootstrap, default value is 3000.

Value

A p*p matrix with values 0 or 1.

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

See Also

individual.test.

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(pc)        # conducting hypothesis test

## Inference on the first subject in population
Res1 = individual.test(pc$ind.est[[1]])

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