Kernel

This function creates a Kernel object using a kernel function (<code>FUN</code>) that takes the distance from the center of the kernel as its first argument.

A collection of data structures and methods for handling volumetric
brain imaging data, with a focus on functional magnetic resonance imaging (fMRI). Provides efficient
representations for three-dimensional and four-dimensional neuroimaging data through sparse and dense array
implementations, memory-mapped file access for large datasets, and
spatial transformation capabilities. Implements methods for image resampling,
spatial filtering, region of interest analysis, and connected component labeling. General introduction
to fMRI analysis can be found in Poldrack et al. (2024, "Handbook of functional MRI data analysis",
<ISBN:9781108795760>).

Bradley Buchsbaum

neuroim2

Data Structures for Brain Imaging Data

Kernel function

<dl><dt>kerndim</dt>
<dd>A numeric vector representing the dimensions in voxels of the kernel.</dd>
<dt>vdim</dt>
<dd>A numeric vector representing the dimensions of the voxels in real units.</dd>
<dt>FUN</dt>
<dd>The kernel function taking its first argument representing the distance from the center of the kernel (default: <code>dnorm</code>).</dd>
<dt>...</dt>
<dd>Additional parameters to the kernel function, <code>FUN</code>.</dd></dl>

Arguments

Create a Kernel object from a function of distance from kernel center — Kernel

<dl>

<dt>kerndim</dt>
<dd>A numeric vector representing the dimensions in voxels of the kernel.</dd>


<dt>vdim</dt>
<dd>A numeric vector representing the dimensions of the voxels in real units.</dd>


<dt>FUN</dt>
<dd>The kernel function taking its first argument representing the distance from the center of the kernel (default: <code>dnorm</code>).</dd>


<dt>...</dt>
<dd>Additional parameters to the kernel function, <code>FUN</code>.</dd>

</dl>

Kernel: Create a Kernel object from a function of distance from kernel center

Description

Usage

Value

Arguments

Examples