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

In BrainCon: Inference the Partial Correlations Based on Time Series Data

The one-sample population inference using Genovese and Wasserman's method

Description

Identify the nonzero partial correlations in one-sample population, based on controlling the rate of the false discovery proportion (FDP) exceeding c0 at \alpha. The method is based on the minimum of the p-values. Input a popEst class object returned by population.est.

Usage

population.test.MinPv(
  popEst,
  alpha = 0.05,
  c0 = 0.1,
  targetSet = NULL,
  simplify = !is.null(targetSet)
)

Arguments

popEst	A popEst class object.
alpha	significance level, default value is 0.05.
c0	threshold of the exceedance rate of FDP, default value is 0.1.
targetSet	a two-column matrix. Each row contains two index corresponding to a pair of variables of interest. If NULL, any pair of two variables is considered to be of interest.
simplify	a logical indicating whether results should be simplified if possible.

Value

If simplify is FALSE, a p*p matrix with values 0 or 1 is returned, and 1 means nonzero.

And if simplify is TRUE, a two-column matrix is returned, indicating the row index and the column index of recovered nonzero partial correlations. Those with lower p values are sorted first.

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.

See Also

population.test.

Examples

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

BrainCon documentation built on May 31, 2023, 6:36 p.m.