Description Usage Arguments Value References See Also Examples

View source: R/individual.test.R

Identify nonzero individual-level partial correlations in time series data
by controlling the exceedance rate of the false discovery proportion (FDP)
at *α=0.05*, considering time dependence.
Input data *X* contains values of p interested
variables over n periods.

1 | ```
individual.test(indEst, alpha = 0.05, c0 = 0.1, MBT = 3000)
``` |

`indEst` |
An |

`alpha` |
significance level, default value is |

`c0` |
threshold of the exceedance rate of the false discovery proportion (FDP),
default value is |

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

A *p*p* matrix with values 0 or 1.
If the j-th row and k-th column of the matrix is 1,
then the partial correlation coefficient between
the j-th variable and the k-th variable is identified to be nonzero.

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`

for making inferences on one individual in the population.

1 2 3 4 | ```
## Quick example for the individual-level inference
data(indsim)
pc = individual.est(indsim) # estimating partial correlation coefficients
Res = individual.test(pc) # conducting hypothesis test
``` |

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