population.est {BrainCon} | R Documentation |

## Estimate population-level partial correlation coefficients

### Description

Estimate population-level partial correlation coefficients in time series data.
And also return coefficients for each individual.
Input time series data for population as a 3-dimensional array or a list.

### Usage

```
population.est(
Z,
lambda = NULL,
type = c("slasso", "lasso"),
alpha = 0.05,
ind.ci = FALSE
)
```

### Arguments

`Z` |
If each individual shares the same number of periods of time, |

`lambda` |
a scalar or a m-length vector, representing the penalty parameters of order |

`type` |
a character string representing the method of estimation. |

`alpha` |
a numeric scalar, default value is |

`ind.ci` |
a logical indicating whether to compute |

### Value

A `popEst`

class object containing two components.

`coef`

a `p*p`

partial correlation coefficients matrix.

`ind.est`

a `m`

-length list, containing estimates for each individuals.

`type`

regression type in estimation.

### 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 population-level estimates
data(popsimA)
# estimating partial correlation coefficients by scaled lasso
pc = population.est(popsimA)
## Inference on the first subject in population
Res_1 = individual.test(pc$ind.est[[1]])
```

*BrainCon*version 0.3.0 Index]