Threshold.RF

Calculates the Random Field theory threshold to give that
 results in a specified p-value.

utilities

Utilizing model-based clustering (unsupervised)
for functional magnetic resonance imaging (fMRI) data.
The developed methods (Chen and Maitra (2023) <doi:10.1002/hbm.26425>)
include 2D and 3D clustering analyses
(for p-values with voxel locations) and
segmentation analyses (for p-values alone) for fMRI data where p-values
indicate significant level of activation responding to stimulate
of interesting. The analyses are mainly identifying active
voxel/signal associated with normal brain behaviors.
Analysis pipelines (R scripts) utilizing this package
(see examples in 'inst/workflow/') is also implemented with high
performance techniques.

Wei-Chen Chen

MixfMRI

Mixture fMRI Clustering Analysis

Ranjan Maitra

Dan Nettleton

Pierre Lafaye De Micheaux

Jonathan L Marchini

Threshold.RF function

<dl> <dt>p.val</dt>
<dd>The required p-value.</dd> <dt>sigma</dt>
<dd>The 3D covariance matrix of the random field.</dd> <dt>voxdim</dt>
<dd>The dimesnions of a voxel.</dd> <dt>num.vox</dt>
<dd>The number of voxels that constitute the random field.</dd> <dt>type</dt>
<dd>The type of random field, "Normal" or "t".</dd> <dt>df</dt>
<dd>The degrees of the t distributed field.</dd></dl>

Arguments

Author

Random Field Theory Thersholds. — Threshold.RF

<dl>

 <dt>p.val</dt>
<dd>The required p-value.</dd>

 <dt>sigma</dt>
<dd>The 3D covariance matrix of the random field.</dd>

 <dt>voxdim</dt>
<dd>The dimesnions of a voxel.</dd>

 <dt>num.vox</dt>
<dd>The number of voxels that constitute the random field.</dd>

 <dt>type</dt>
<dd>The type of random field, "Normal" or "t".</dd>

 <dt>df</dt>
<dd>The degrees of the t distributed field.</dd>

</dl>

Threshold.RF: Random Field Theory Thersholds.

Description

Usage

Value

Arguments

Author

Details

See Also

Examples