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

population2sample.test.MinPv | R Documentation |

Identify differences of partial correlations between two populations
in two groups of time series data,
based on controlling the rate of the false discovery proportion (FDP) exceeding *c0*
at *α*. The method is based on the minimum of the p-values.
Input two `popEst`

class objects returned by `population.est`

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

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

`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 |

`simplify` |
a logical indicating whether results should be simplified if possible. |

If `simplify`

is `FALSE`

, a *p*p* matrix with values 0 or 1 is returned, and 1 means 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.
Those with lower p values are sorted first.

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.

## 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.MinPv(pc1, pc2)

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