Description Usage Arguments Value Examples

This function utilizes Dynamic Connectivity Regression (DCR) algorithm proposed by Cribben el al. (2012) to test the equality of connectivity in two fMRI signals.

1 2 3 4 5 6 7 8 9 10 | ```
testGlasso(
subY1,
subY2,
p,
lambda = "bic",
nboot = 100,
n.cl,
bound = c(0.001, 1),
gridTF = FALSE
)
``` |

`subY1` |
a sample of size length*dim |

`subY2` |
a sample of size length*dim |

`p` |
Gep(p) distribution controls the size of stationary bootstrap. The mean block length is 1/p |

`lambda` |
two selections possible for optimal parameter of lambda. "bic" finds lambda from bic criteria, or user can directly input the penalty value. |

`nboot` |
the number of bootstrap sample for pvalue. Default is 100. |

`n.cl` |
number of cores in parallel computing. The default is (machine cores - 1) |

`bound` |
bound of bic search in "bic" rule. Default is (.001, 1) |

`gridTF` |
Utilize a grid search to optimize hyperparameters |

**pval** The empirical p-value for testing the equality of connectivity structure

**rho** The sequence of penalty paramter based on the combined sample, subY1 and subY2.

**fit0** Output of glasso for combied sample

**fit1** Output of glasso for subY1

**fit2** Output of glasso for subY2

1 | ```
test1= testGlasso(testsim$X, testsim$Y, n.cl=1)
``` |

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