SingleVoxelFETS {BayesDLMfMRI} | R Documentation |

## SingleVoxelFETS

### Description

This function is used to perform an activation analysis for single voxels based on the FETS algorithm.

### Usage

```
SingleVoxelFETS(
posi.ffd,
covariates,
ffdc,
m0,
Cova,
delta,
S0,
n0,
N1,
Nsimu1,
Cutpos1,
Min.vol,
r1,
Test
)
```

### Arguments

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

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

`ffdc` |
a 4D array ( |

`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 among 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. |

`Cutpos1` |
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 ( |

`Min.vol` |
helps to define a threshold for the voxels considered in
the analysis. For example, |

`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 |

`Test` |
test type either |

### Details

This function allows the development of an 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).

### Value

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`

).

### References

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.

### Examples

```
## Not run:
# This example can take a long time to run.
fMRI.data <- get_example_fMRI_data()
data("covariates", package="BayesDLMfMRI")
res.indi <- SingleVoxelFETS(posi.ffd = c(14, 56, 40),
covariates = Covariates,
ffdc = fMRI.data,
m0 = 0, Cova = 100, delta = 0.95, S0 = 1,
n0 = 1, Nsimu1 = 100, N1 = FALSE, Cutpos1 = 30,
Min.vol = 0.10, r1 = 1, Test = "LTT")
## End(Not run)
```

*BayesDLMfMRI*version 0.0.3 Index]