plot.onset_data

Divide trials into which AOI participants started on; plot proportion looking away from that AOI.

Addresses tasks along the pipeline from raw
data to analysis and visualization for eye-tracking data. Offers several
popular types of analyses, including linear and growth curve time analyses,
onset-contingent reaction time analyses, as well as several non-parametric
bootstrapping approaches. For references to the approach see Mirman,
Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and
Barr (2008) <doi:10.1016/j.jml.2007.09.002>.

Samuel Forbes

eyetrackingR

Eye-Tracking Data Analysis

Jacob Dink

Brock Ferguson

plot.onset_data function

<dl><dt>x</dt>
<dd>The output of the <code>make_onset_data</code> function</dd>
<dt>predictor_columns</dt>
<dd>Column(s) by which to facet the data. Maximum two columns. Will perform
median split if numeric.</dd>
<dt>...</dt>
<dd>Ignored</dd></dl>

Arguments

Plot onset-contingent data — plot.onset_data

<dl>

<dt>x</dt>
<dd>The output of the <code>make_onset_data</code> function</dd>


<dt>predictor_columns</dt>
<dd>Column(s) by which to facet the data. Maximum two columns. Will perform
median split if numeric.</dd>


<dt>...</dt>
<dd>Ignored</dd>

</dl>

plot.onset_data: Plot onset-contingent data

Description

Usage

Value

Arguments