GroupSingleVoxelFSTS {BayesDLMfMRI} | R Documentation |

This function is used to perform a group activation analysis for single voxels based on the FSTS algorithm.

```
GroupSingleVoxelFSTS(
posi.ffd,
DatabaseGroup,
covariates,
m0,
Cova,
delta,
S0,
n0,
N1,
Nsimu1,
r1,
Cutpos
)
```

`posi.ffd` |
the position of the voxel in the brain image. |

`DatabaseGroup` |
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. |

`r1` |
a 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 |

`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 ( |

This function allows the performance of a group activation analysis for single voxels. A multivariate dynamic linear model is fitted to a cluster of voxels, with its center at location (i,j,k), in the way it is presented in (Cardona-Jiménez and de B. Pereira 2021).

a list containing a vector (`Evidence`

) with the evidence measure of
activation for each of the `p`

covariates considered in the model, the simulated
online trajectories related to the state parameter, the simulated BOLD responses,
and a measure to examine the goodness of fit of the model \((100 \ast |Y[i,j,k]_t - \hat{Y}[i,j,k]_t |/ \hat{Y}[i,j,k]_t )\) for that particular voxel (`FitnessV`

).

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:
# This example can take a long time to run.
DatabaseGroup <- get_example_fMRI_data_group()
data("covariates", package="BayesDLMfMRI")
resSingle <- GroupSingleVoxelFSTS(posi.ffd = c(14, 56, 40), DatabaseGroup,
covariates = Covariates, m0 = 0, Cova = 100,
delta = 0.95, S0 = 1, n0 = 1, N1 = FALSE,
Nsimu1 = 100, r1 = 1, Cutpos = 30)
## End(Not run)
```

[Package *BayesDLMfMRI* version 0.0.3 Index]