population2sample.test.MinPv {BrainCon}

Identify differences of partial correlations between two populations using Genovese and Wasserman's method

Description

Identify differences of partial correlations between two populations in two groups of time series data, 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 two popEst class objects returned by population.est (the number of individuals in two groups can be different).

Usage

population2sample.test.MinPv(
  popEst1,
  popEst2,
  alpha = 0.05,
  c0 = 0.1,
  targetSet = NULL,
  simplify = !is.null(targetSet)
)

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 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 unequal.

And if simplify is TRUE, a two-column matrix is returned, indicating the row index and the column index of recovered unequal 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.

Examples

## Quick example for the two-sample case inference
data(popsimA)
data(popsimB)
# estimating partial correlation coefficients by lasso (scaled lasso does the same)
pc1 = population.est(popsimA, type = 'l')
pc2 = population.est(popsimB, type = 'l')
# conducting hypothesis test
Res = population2sample.test.MinPv(pc1, pc2)