setupConvolve

convolve

convolveFIR

This function estimates the basis vectors related to responses following 
a binary impulse and convolves that binary impulse vector.

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

setupConvolve function

<dl><dt>ts_list</dt>
<dd>a list of dataframes.</dd>
<dt>varLabels</dt>
<dd>a list of variable sets. Contains varLabels$coln, all column names, varLabels$conv, 
the names of variables to convolve, and varLabels$exog, a list of exogenous variables (if any).</dd>
<dt>conv_length</dt>
<dd>Expected response length in seconds. For functional MRI BOLD, 16 seconds (default) is typical
for the hemodynamic response function.</dd>
<dt>conv_interval</dt>
<dd>Interval between data acquisition. Currently must be a scalar For 
fMRI studies, this is the repetition time. Defaults to 1.</dd></dl>

Arguments

Group iterative multiple model estimation. — setupConvolve

<dl>

<dt>ts_list</dt>
<dd>a list of dataframes.</dd>


<dt>varLabels</dt>
<dd>a list of variable sets. Contains varLabels$coln, all column names, varLabels$conv, 
the names of variables to convolve, and varLabels$exog, a list of exogenous variables (if any).</dd>


<dt>conv_length</dt>
<dd>Expected response length in seconds. For functional MRI BOLD, 16 seconds (default) is typical
for the hemodynamic response function.</dd>


<dt>conv_interval</dt>
<dd>Interval between data acquisition. Currently must be a scalar For 
fMRI studies, this is the repetition time. Defaults to 1.</dd>

</dl>

setupConvolve: Group iterative multiple model estimation.

Description

Usage

Arguments