summaryPathsCounts

Create summary matrix of path counts and subgroup plots

internal

Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.

KM Gates

gimme

Group Iterative Multiple Model Estimation

summaryPathsCounts function

<dl><dt>dat</dt>
<dd>A list containing information created in setup().</dd>
<dt>grp</dt>
<dd>A list containing group-level information. NULL in aggSEM and
indSEM.</dd>
<dt>store</dt>
<dd>A list containing output from indiv.search().</dd>
<dt>sub</dt>
<dd>A list containing subgroup information.</dd>
<dt>sub_spec</dt>
<dd>A list containing information specific to each subgroup.</dd></dl>

Arguments

Create summary matrix of path counts and subgroup plots — summaryPathsCounts

<dl>

<dt>dat</dt>
<dd>A list containing information created in setup().</dd>


<dt>grp</dt>
<dd>A list containing group-level information. NULL in aggSEM and
indSEM.</dd>


<dt>store</dt>
<dd>A list containing output from indiv.search().</dd>


<dt>sub</dt>
<dd>A list containing subgroup information.</dd>


<dt>sub_spec</dt>
<dd>A list containing information specific to each subgroup.</dd>

</dl>

summaryPathsCounts: Create summary matrix of path counts and subgroup plots

Description

Usage

Value

Arguments