Description Usage Arguments Value Author(s) References Examples

The main function for brain imaging All-Resolutions Inference (ARI) method based on critical vectors constructed
using the p-values permutation distribution. The function computes simultaneous lower bounds for the number of true discoveries
for each set of hypotheses specified in `ix`

controlling family-wise error rate.

1 2 3 4 |

`copes` |
list of NIfTI file. The list of copes, i.e., constrasts maps, one for each subject used to compute the statistical tests. |

`thr` |
numeric value. Threshold used to construct the cluster map. Default @NULL. |

`mask` |
NIfTI file or character string. 3D array of logical values (i.e. |

`alpha` |
numeric value in '[0,1]'. It expresses the alpha level to control the family-wise error rate. Default 0.05. |

`clusters` |
NIfTI file or character string. 3D array of cluster ids (0 when voxel does not belong to any cluster) or a (character) NIfTI file name.
If |

`alternative` |
character string. It refers to the alternative hypothesis, must be one of |

`summary_stat` |
character string. Choose among |

`silent` |
Boolean value. Default @FALSE. If @TRUE the function prints the results. |

`family` |
string character. Choose a family of confidence envelopes to compute the critical vector
from |

`delta` |
numeric value. It expresses the delta value, please see the references. Default to 0. |

`B` |
numeric value. Number of permutations, default to 1000. |

`rand` |
Boolean value. Default @FALSE. If |

`iterative` |
Boolean value. If |

`approx` |
Boolean value. Default @TRUE. If you are treating high dimensional data, we suggest to put |

`ncomb` |
Numeric value. If |

`step.down` |
Boolean value. Default @FALSE If you want to compute the lambda calibration parameter using the step-down approach put @TRUE. |

`max.step` |
Numeric value. Default to 10. Maximum number of steps for the step down approach, so useful when |

`...` |
further arguments. See |

A list with elements - out: data.frame containing the size, the number of false null hypotheses, the number of true null hypotheses, the lower bound for the true discovery proportion, and other statistics for each cluster. - clusters: matrix describing the clusters analyzed.

Angela Andreella

For the general framework of All-Resolutions Inference see:

Goeman, Jelle J., and Aldo Solari. "Multiple testing for exploratory research." Statistical Science 26.4 (2011): 584-597.

For All-Resolutions Inference for functional Magnetic Resonance Imaging data see:

Rosenblatt, Jonathan D., et al. "All-resolutions inference for brain imaging." Neuroimage 181 (2018): 786-796.

For permutation-based All-Resolutions Inference see:

Andreella, Angela, et al. "Permutation-based true discovery proportions for fMRI cluster analysis." arXiv preprint arXiv:2012.00368 (2020).

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
## Not run:
library(remotes)
install_github("angeella/fMRIdata")
library(fMRIdata)
data(Auditory_clusterTH3_2)
data(Auditory_copes)
data(Auditory_mask)
auditory_out <- pARIbrain(copes = Auditory_copes,
clusters = Auditory_clusterTH3_2, mask = Auditory_mask,
alpha = 0.05, silent = TRUE)
auditory_out$out
## End(Not run)
``` |

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