sim18ccfa40

Categorical CFA data simulated based on 18 items, 3 factors, and 4 categories
with local independence and 10 percent missingness at random; factorial correlation \(\Phi=.3\).

datasets

A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.

Jinsong Chen

LAWBL

Latent (Variable) Analysis with Bayesian Learning

sim18ccfa40 function

A list with components:<dl class="dl-horizontal">
<dt><code>dat</code></dt><dd>A dataset with simulated responses of 1000 individuals to 18 items</dd>
<dt><code>qlam</code></dt><dd>Loading pattern and values used to simulated the data</dd>
</dl>

Format

Simulated CCFA data with LI and missingness — sim18ccfa40

A list with components:<dl class='dl-horizontal'>
<dt><code>dat</code></dt><dd>A dataset with simulated responses of 1000 individuals to 18 items</dd>
<dt><code>qlam</code></dt><dd>Loading pattern and values used to simulated the data</dd>
</dl>

sim18ccfa40: Simulated CCFA data with LI and missingness

Description

Usage

Arguments

Format