ffdGroupEvidenceFETS {BayesDLMfMRI} | R Documentation |

This function can be used to build activation maps for group task-based fMRI data.

```
ffdGroupEvidenceFETS(
ffdGroup,
covariates,
m0 = 0,
Cova = 100,
delta = 0.95,
S0 = 1,
n0 = 1,
N1 = FALSE,
Nsimu1 = 100,
Cutpos = 30,
r1,
Test,
mask,
Ncores = NULL
)
```

`ffdGroup` |
list of N elements, each being a 4D array ( |

`covariates` |
a data frame or matrix whose columns contain the covariates related to the expected BOLD response obtained from the experimental setup. |

`m0` |
the constant prior mean value for the covariates parameters and common to all voxels within every neighborhood at |

`Cova` |
a positive constant that defines the prior variances for the covariates parameters at |

`delta` |
a discount factor related to the evolution variances. Recommended values between |

`S0` |
prior covariance structure between pair of voxels within every cluster at |

`n0` |
a positive hyperparameter of the prior distribution for the covariance matrix |

`N1` |
is the number of images ( |

`Nsimu1` |
is the number of simulated on-line trajectories related to the state parameters. These simulated curves are later employed to compute the posterior probability of voxel activation. |

`Cutpos` |
a cutpoint time from where the on-line trajectories begin. This parameter value is related to an approximation from a t-student distribution to a normal distribution. Values equal to or greater than 30 are recommended ( |

`r1` |
positive integer number that defines the distance from every voxel with its most distant neighbor. This value determines the size of the cluster. The users can set a range of different |

`Test` |
test type either |

`mask` |
a 3D array that works as a brain of reference (MNI atlas) for the group analysis. |

`Ncores` |
a positive integer indicating the number of threads or cores to be used in the computation of the activation maps. |

A multivariate dynamic linear model is fitted in the same fashion as at the individual level for every subject in the sample. However, at this stage, the posterior distributions from all the subjects are combined to build a single one, which is then employed to compute the activation evidence maps for the group using Forward estimated trajectories sampler (FETS) algorithm. To deeply understand the method implemented in this package, a reading of (Cardona-Jiménez and de B. Pereira 2021) and (Cardona-Jiménez 2021) is mandatory.

It returns a list of \(2 \times p\) elements, where \(p\) is the number of covariates, and 2 is the number
of options evaluated as sampler distributions: Average cluster effect and Marginal effect (when `Test=="LTT"`

) or Joint effect and Marginal effect (when `Test=="JointTest"`

). The first `p`

elements from the list are
the activation maps related to each column of the covariates matrix respectively when computing the activation evidence using either
`Test=="LTT"`

or `Test=="JointTest"`

. The remaining activation maps are those associated with the marginal distribution.

Cardona-Jiménez J, de B. Pereira CA (2021).
“Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: An application to task-based fMRI data analysis.”
*Computational Statistics & Data Analysis*, **163**, 107297.
ISSN 0167-9473, doi:10.1016/j.csda.2021.107297, https://www.sciencedirect.com/science/article/pii/S0167947321001316.

Cardona-Jiménez J (2021).
“BayesDLMfMRI: Bayesian Matrix-Variate Dynamic Linear Models for Task-based fMRI Modeling in R.”
*arXiv e-prints*, arXiv–2111.

```
## Not run:
DatabaseGroup <- get_example_fMRI_data_group()
data("covariates", package="BayesDLMfMRI")
data("mask", package="BayesDLMfMRI")
res <- ffdGroupEvidenceFETS(ffdGroup = DatabaseGroup, covariates = Covariates,
m0 = 0, Cova = 100, delta = 0.95, S0 = 1,
n0 = 1, N1 = FALSE, Nsimu1 = 100, Cutpos=30,
r1 = 1, Test = "JointTest", mask = mask, Ncores = 7)
str(res)
## End(Not run)
```

[Package *BayesDLMfMRI* version 0.0.3 Index]