Description Usage Arguments Value References Examples

View source: R/population2sample.test.MinPv.R

Identify differences of partial correlations between two populations
in two groups of time series data
by controlling the exceedance rate of the false discovery proportion (FDP)
at *α=0.05*. The method is based on the minimum of the p-values.
Input two groups of data *Z_1* and *Z_2*, each contains values of p interested
variables of individuals
(the number of individuals in two groups can be different) over n periods.

1 | ```
population2sample.test.MinPv(popEst1, popEst2, alpha = 0.05, c0 = 0.1)
``` |

`popEst1` |
A |

`popEst2` |
A |

`alpha` |
significance level, default value is |

`c0` |
threshold of the exceedance rate of the false discovery proportion (FDP),
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 coefficients between
the j-th variable and the k-th variable in two populations
are identified to be unequal.

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

1 2 3 4 5 6 | ```
## Quick example for the two-sample case inference
data(popsimA)
data(popsimB)
pc1 = population.est(popsimA)
pc2 = population.est(popsimB)
Res = population2sample.test.MinPv(pc1, pc2) # conducting hypothesis test
``` |

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