e3_smooth: Generate datasets with smoothing for MixfMRI simulations
In MixfMRI: Mixture fMRI Clustering Analysis
Smoothing R Documentation
Generate datasets with smoothing for MixfMRI simulations
Description
Generate datasets with smoothing for MixfMRI simulations
Usage
gcv.smooth2d(y, interval)
Arguments
y
a set of p-values in 2d phantom
interval
an interval for optimize function.
Details
The function is used to smooth for Dr. Maitra's 2d phantom simulation.
The smoothing method is based on Garcia (2010), CSDA.
Value
Return a list containing two elements im.smooth and
par.val.
Author(s)
Ranjan Maitra.
References
Chen, W.-C. and Maitra, R. (2021)
“A Practical Model-based Segmentation Approach for Accurate
Activation Detection in Single-Subject functional Magnetic
Resonance Imaging Studies”,
arXiv:2102.03639.
MixfMRI documentation built on Sept. 8, 2023, 5:06 p.m.
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| Smoothing | R Documentation |
Generate datasets with smoothing for MixfMRI simulations
Description
Generate datasets with smoothing for MixfMRI simulations
Usage
gcv.smooth2d(y, interval)
Arguments
y |
a set of p-values in 2d phantom |
interval |
an interval for |
Details
The function is used to smooth for Dr. Maitra's 2d phantom simulation. The smoothing method is based on Garcia (2010), CSDA.
Value
Return a list containing two elements im.smooth and
par.val.
Author(s)
Ranjan Maitra.
References
Chen, W.-C. and Maitra, R. (2021) “A Practical Model-based Segmentation Approach for Accurate Activation Detection in Single-Subject functional Magnetic Resonance Imaging Studies”, arXiv:2102.03639.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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