# population.test.MinPv: The one-sample population inference using Genovese and... In BrainCon: Inference the Partial Correlations Based on Time Series Data

 population.test.MinPv R Documentation

## 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 α. 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.

`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 April 21, 2022, 5:08 p.m.