interpolate

Linear interpolation of time series data. The intended use of this method
is for filling in missing pupil samples (NAs) in the time series. This method
uses "na.approx()" function from the zoo package, which implements linear
interpolation using the "approx()" function from the stats package.
Currently, NAs at the beginning and the end of the data are replaced with
values on either end, respectively, using the "rule = 2" argument in the
<code><a href="/link/approx()?package=eyeris&version=2.0.0" data-mini-rdoc="eyeris::approx()">approx()</a></code> function.

Pupillometry offers a non-invasive window into the mind and has been used extensively as a psychophysiological readout of arousal signals linked with cognitive processes like attention, stress, and emotional states [Clewett et al. (2020) <doi:10.1038/s41467-020-17851-9>; Kret & Sjak-Shie (2018) <doi:10.3758/s13428-018-1075-y>; Strauch (2024) <doi:10.1016/j.tins.2024.06.002>]. Yet, despite decades of pupillometry research, many established packages and workflows to date lack design patterns based on Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [see Wilkinson et al. (2016) <doi:10.1038/sdata.2016.18>]. 'eyeris' provides a modular, performant, and extensible preprocessing framework for pupillometry data with BIDS-like organization and interactive output reports [Esteban et al. (2019) <doi:10.1038/s41592-018-0235-4>; Gorgolewski et al. (2016) <doi:10.1038/sdata.2016.44>]. Development was supported, in part, by the Stanford Wu Tsai Human Performance Alliance, Stanford Ric Weiland Graduate Fellowship, Stanford Center for Mind, Brain, Computation and Technology, NIH National Institute on Aging Grants (R01-AG065255, R01-AG079345), NSF GRFP (DGE-2146755), McKnight Brain Research Foundation Clinical Translational Research Scholarship in Cognitive Aging and Age-Related Memory Loss, American Brain Foundation, and the American Academy of Neurology.

Shawn Schwartz

eyeris

Flexible, Extensible, & Reproducible Pupillometry Preprocessing

interpolate function

<dl><dt>eyeris</dt>
<dd>An object of class <code>eyeris</code> derived from <code>load_asc()</code></dd>
<dt>verbose</dt>
<dd>A flag to indicate whether to print detailed logging messages.
Defaults to <code>TRUE</code>. Set to <code>FALSE</code> to suppress messages about the current
processing step and run silently</dd>
<dt>call_info</dt>
<dd>A list of call information and parameters. If not provided,
it will be generated from the function call</dd></dl>

Arguments

Linear interpolation of time series data. The intended use of this method
is for filling in missing pupil samples (NAs) in the time series. This method
uses "na.approx()" function from the zoo package, which implements linear
interpolation using the "approx()" function from the stats package.
Currently, NAs at the beginning and the end of the data are replaced with
values on either end, respectively, using the "rule = 2" argument in the
<code><a href='https://rdrr.io/r/stats/approxfun.html'>approx()</a></code> function.

Interpolate missing pupil samples — interpolate

<dl>

<dt>eyeris</dt>
<dd>An object of class <code>eyeris</code> derived from <code>load_asc()</code></dd>


<dt>verbose</dt>
<dd>A flag to indicate whether to print detailed logging messages.
Defaults to <code>TRUE</code>. Set to <code>FALSE</code> to suppress messages about the current
processing step and run silently</dd>


<dt>call_info</dt>
<dd>A list of call information and parameters. If not provided,
it will be generated from the function call</dd>

</dl>

interpolate: Interpolate missing pupil samples

Description

Usage

Value

Arguments

Details

See Also

Examples