generate_model

Generate matrices of true model parameters for the supported true models.
These matrices are intended to passed to the <code>model_matrix</code> argument of
<code>powerly()</code>.

An implementation of the sample size computation method for network
models proposed by Constantin et al. (2021) <doi:10.31234/osf.io/j5v7u>.
The implementation takes the form of a three-step recursive algorithm
designed to find an optimal sample size given a model specification and a
performance measure of interest. It starts with a Monte Carlo simulation
step for computing the performance measure and a statistic at various sample
sizes selected from an initial sample size range. It continues with a
monotone curve-fitting step for interpolating the statistic across the entire
sample size range. The final step employs stratified bootstrapping to quantify
the uncertainty around the fitted curve.

Mihai Constantin

powerly

Sample Size Analysis for Psychological Networks and More

generate_model function

<dl><dt>type</dt>
<dd>Character string representing the type of true model. Possible
values are <code>"ggm"</code> (the default).</dd>
<dt>...</dt>
<dd>Required arguments used for the generation of the true model. See
the True Models section of <code>powerly()</code> for the arguments
required for each true model.</dd></dl>

Arguments

Generate true model parameters — generate_model

<dl>

<dt>type</dt>
<dd>Character string representing the type of true model. Possible
values are <code>"ggm"</code> (the default).</dd>


<dt>...</dt>
<dd>Required arguments used for the generation of the true model. See
the True Models section of <code>powerly()</code> for the arguments
required for each true model.</dd>

</dl>

generate_model: Generate true model parameters

Description

Usage

Value

Arguments

See Also