population.test.MinPv: 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)
)

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.

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.

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

Run this code

## 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)