gcv.smooth2d

Generate datasets with smoothing for MixfMRI simulations

programming

Smoothing

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

Smoothing function

<dl> <dt>y</dt>
<dd>a set of p-values in 2d phantom</dd> <dt>interval</dt>
<dd>an interval for <code>optimize</code> function.</dd></dl>

Arguments

Author

Generate datasets with smoothing for MixfMRI simulations — Smoothing

<dl>

 <dt>y</dt>
<dd>a set of p-values in 2d phantom</dd>

 <dt>interval</dt>
<dd>an interval for <code>optimize</code> function.</dd>

</dl>

Smoothing: Generate datasets with smoothing for MixfMRI simulations

Description

Usage

Value

Arguments

Author

Details

References