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‎llms.txt‎

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## All functions
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- [`ModelArray()`](https://pennlinc.github.io/ModelArray/reference/ModelArray.md)
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: Construct a ModelArray object
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: ModelArray class
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- [`ModelArray.gam()`](https://pennlinc.github.io/ModelArray/reference/ModelArray.gam.md)
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: Run GAM for element-wise data
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- [`ModelArray.lm()`](https://pennlinc.github.io/ModelArray/reference/ModelArray.lm.md)

‎pkgdown.yml‎

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installations: installations.html
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modelling: modelling.html
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walkthrough: walkthrough.html
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last_built: 2026-03-27T22:38Z
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last_built: 2026-03-28T00:35Z
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urls:
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reference: https://pennlinc.github.io/ModelArray/reference
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article: https://pennlinc.github.io/ModelArray/articles

‎reference/ModelArray-class.html‎

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<html>
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<head>
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<meta http-equiv="refresh" content="0;URL=https://pennlinc.github.io/ModelArray/reference/ModelArray.html" />
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<meta name="robots" content="noindex">
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<link rel="canonical" href="https://pennlinc.github.io/ModelArray/reference/ModelArray.html">
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</head>
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</html>
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‎reference/ModelArray.html‎

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‎reference/ModelArray.md‎

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# Construct a ModelArray object
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# ModelArray class
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Load element-wise data from an .h5 file as a \`ModelArray\` object.
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A ModelArray wraps one or more element-wise scalar matrices (e.g., FD,
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FC, log_FC for fixel data) read lazily via DelayedArray, along with any
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previously saved analysis results. The object holds references to the
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underlying HDF5 file and reads data on demand, making it suitable for
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large-scale neuroimaging datasets.
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Reads scalar matrices and (optionally) saved analysis results from an
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HDF5 file and returns a ModelArray object.
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## Usage
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``` r
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ModelArray(filepath, scalar_types = c("FD"), analysis_names = character(0))
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ModelArray(filepath, scalar_types = c("FD"), analysis_names = character(0))
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```
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## Arguments
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- filepath:
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Path to an .h5 file
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Character. Path to an existing HDF5 (`.h5`) file containing
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element-wise scalar data.
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- scalar_types:
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Expected scalars
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Character vector. Names of scalar groups to read from `/scalars/` in
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the HDF5 file. Default is `c("FD")`. Must match group names in the
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file; verify with `rhdf5::h5ls(filepath)`.
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- analysis_names:
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The subfolder names for results in the .h5 file. If empty (default),
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results are not read.
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Character vector. Subfolder names under `/results/` to load. Default
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is `character(0)` (no results loaded).
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## Value
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A \`ModelArray\` object
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A ModelArray object.
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## Details
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ModelArray is an S4 class that represents element-wise scalar data and
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associated statistical results backed by an HDF5 file on disk.
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Each scalar in the HDF5 file is stored at `/scalars/<name>/values` as a
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matrix of elements (rows) by source files (columns). Source filenames
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are read from HDF5 attributes or companion datasets. Analysis results,
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if present, live under `/results/<analysis_name>/results_matrix`.
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ModelArray objects are typically created with the `ModelArray`
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constructor function. Element-wise models are fit with
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[`ModelArray.lm`](https://pennlinc.github.io/ModelArray/reference/ModelArray.lm.md),
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[`ModelArray.gam`](https://pennlinc.github.io/ModelArray/reference/ModelArray.gam.md),
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or
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[`ModelArray.wrap`](https://pennlinc.github.io/ModelArray/reference/ModelArray.wrap.md).
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The constructor reads each scalar listed in `scalar_types` from
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`/scalars/<scalar_type>/values` in the HDF5 file, wrapping them as
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DelayedArray objects for lazy access. Source filenames are extracted
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from HDF5 attributes or companion datasets.
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If `analysis_names` is non-empty, saved results are read from
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`/results/<name>/results_matrix` along with column name metadata.
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**Debugging tip:** If you encounter
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`"Error in h(simpleError(msg, call)) : error in evaluating the argument 'seed'..."`,
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check that `scalar_types` matches the groups present in the file.
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Inspect with `rhdf5::h5ls(filepath)`.
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## Slots
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- `sources`:
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A named list of character vectors. Each element corresponds to a
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scalar and contains the source filenames (one per input file/subject).
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- `scalars`:
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A named list of DelayedArray matrices. Each matrix has elements as
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rows and source files as columns.
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- `results`:
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A named list of analysis results. Each element is itself a list
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containing at minimum `results_matrix` (a DelayedArray).
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- `path`:
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Character. Path(s) to the HDF5 file(s) on disk.
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## See also
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`ModelArray` for the constructor,
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[`ModelArray.lm`](https://pennlinc.github.io/ModelArray/reference/ModelArray.lm.md),
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[`ModelArray.gam`](https://pennlinc.github.io/ModelArray/reference/ModelArray.gam.md),
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[`ModelArray.wrap`](https://pennlinc.github.io/ModelArray/reference/ModelArray.wrap.md)
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for analysis,
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[`scalars`](https://pennlinc.github.io/ModelArray/reference/scalars.md),
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[`sources`](https://pennlinc.github.io/ModelArray/reference/sources.md),
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[`results`](https://pennlinc.github.io/ModelArray/reference/results.md)
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for accessors.
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ModelArray for the class definition,
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[`h5summary`](https://pennlinc.github.io/ModelArray/reference/h5summary.md)
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for inspecting an HDF5 file without loading,
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[`scalars`](https://pennlinc.github.io/ModelArray/reference/scalars.md),
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[`sources`](https://pennlinc.github.io/ModelArray/reference/sources.md)
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for accessing loaded data.
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## Examples
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``` r
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if (FALSE) { # \dontrun{
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ma <- ModelArray("path/to/data.h5", scalar_types = c("FD", "FC"))
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ma
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scalars(ma)
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} # }
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```

‎reference/index.html‎

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‎reference/index.md‎

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## All functions
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- [`ModelArray()`](https://pennlinc.github.io/ModelArray/reference/ModelArray.md)
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: Construct a ModelArray object
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: ModelArray class
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- [`ModelArray.gam()`](https://pennlinc.github.io/ModelArray/reference/ModelArray.gam.md)
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: Run GAM for element-wise data
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- [`ModelArray.lm()`](https://pennlinc.github.io/ModelArray/reference/ModelArray.lm.md)

‎search.json‎

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