testGlasso | R Documentation |

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

```
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 p-value. 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 parameter based on the combined sample, subY1 and subY2.

**fit0** Output of glasso for combined sample

**fit1** Output of glasso for subY1

**fit2** Output of glasso for subY2

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

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.