population2sample.test {BrainCon} | R Documentation |

## Identify differences of partial correlations between two populations

### Description

Identify differences of partial correlations between two populations
in two groups of time series data by
controlling the rate of the false discovery proportion (FDP) exceeding `c0`

at `\alpha`

, considering time dependence.
Input two `popEst`

class objects returned by `population.est`

(the number of individuals in two groups can be different).

### Usage

```
population2sample.test(
popEst1,
popEst2,
alpha = 0.05,
c0 = 0.1,
targetSet = NULL,
MBT = 5000,
simplify = !is.null(targetSet)
)
```

### Arguments

`popEst1` |
A |

`popEst2` |
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.
If the j-th row and k-th column of the matrix is 1,
then the partial correlation coefficients between
the j-th variable and the k-th variable in two populations
are identified to be 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.
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.

### 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(pc1, pc2)
# conducting hypothesis test and returning simplified results
Res_s = population2sample.test(pc1, pc2, simplify = TRUE)
```

*BrainCon*version 0.3.0 Index]