.validate_input {BayesDLMfMRI}R Documentation

.validate_input

Description

validate input

Usage

.validate_input(
  N1 = NULL,
  Test = NULL,
  Nsimu1 = NULL,
  ffdc = NULL,
  covariates = NULL,
  r1 = NULL,
  delta = NULL,
  perVol = NULL,
  Min.vol = NULL,
  n0 = NULL,
  Cutpos1 = NULL,
  ffdGroup = NULL
)

Arguments

N1

is the number of images (2<N1<T) from the ffdc array employed in the model fitting.N1=NULL (or equivalently N1=T) is its default value, taking all the images in the ffdc array for the fitting process.

Test

test type either "LTT" (Average cluster effect) or "JointTest" (Joint effect).

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.

ffdc

a 4D array (ffdc[i,j,k,t]) that contains the sequence of MRI images that are meant to be analyzed. (i,j,k) define the position of the observed voxel at time t.

covariates

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

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 r values: r = 0, 1, 2, 3, 4, which leads to q = 1, 7, 19, 27, 33, where q is the size of the cluster.

delta

a discount factor related to the evolution variances. Recommended values between 0.85<delta<1. delta=1 will yield results similar to the classical general linear model.

perVol

helps to define a threshold for the voxels considered in the analysis. For example, Min.vol = 0.10 means that all the voxels with values below to max(ffdc)*perVol can be considered irrelevant and discarded from the analysis.

Min.vol

helps to define a threshold for the voxels considered in the analysis. For example, Min.vol = 0.10 means that all the voxels with values below to max(ffdc)*Min.vol can be considered irrelevant and discarded from the analysis.

n0

a positive hyperparameter of the prior distribution for the covariance matrix S0 at t=0 (n=1 is the default value when no prior information is available). For the case of available prior information, n0 can be set as n0=np, where np is the number of MRI images in the pilot sample.

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 (30<Cutpos1<T).

ffdGroup

group


[Package BayesDLMfMRI version 0.0.3 Index]