population.test {BrainCon} | R Documentation |

## The one-sample population inference

### 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`

, considering time dependence.
Input a `popEst`

class object returned by `population.est`

.

### Usage

```
population.test(
popEst,
alpha = 0.05,
c0 = 0.1,
targetSet = NULL,
MBT = 5000,
simplify = !is.null(targetSet)
)
```

### Arguments

`popEst` |
A |

`alpha` |
significance level, default value is |

`c0` |
threshold of the exceedance rate of FDP,
default value is |

`targetSet` |
a two-column matrix. Each row contains two index corresponding to a pair of variables of interest.
If |

`MBT` |
times of multiplier bootstrap, default value is |

`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.
We only retain the results which the row index is less than the column index.
Those with larger test statistics are sorted first.

### 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

### Examples

```
## Quick example for the one-sample population inference
data(popsimA)
# estimating partial correlation coefficients by scaled lasso
pc = population.est(popsimA)
# conducting hypothesis test
Res = population.test(pc)
# conducting hypothesis test in variables of interest
set = cbind(rep(7:9, each = 10), 1:10)
Res_like = population.test(pc, targetSet = set)
```

*BrainCon*version 0.3.0 Index]