data_sim_pcirt

<p>The dataset contains the simulation setting and the response data.</p>

datasets

Provides stochastic EM algorithms for latent variable models
with a high-dimensional latent space. So far, we provide functions for confirmatory item
factor analysis based on the multidimensional two parameter logistic (M2PL) model and the
generalized multidimensional partial credit model. These functions scale well for problems
with many latent traits (e.g., thirty or even more) and are virtually tuning-free.
The computation is facilitated by multiprocessing 'OpenMP' API.
For more information, please refer to:
Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale
Full-information Item Factor Analysis. British Journal of Mathematical and Statistical
Psychology. <doi:10.1111/bmsp.12153>.

Siliang Zhang

lvmcomp

Stochastic EM Algorithms for Latent Variable Models with a
High-Dimensional Latent Space

Yunxiao Chen

Jorge Nocedal

Naoaki Okazaki

data_sim_pcirt function

<p>An object of class <code>list</code> of length 10.</p>

Format

Simulated dataset for generalized partial credit model. — data_sim_pcirt

Simulated dataset for generalized partial credit model.

data_sim_pcirt: Simulated dataset for generalized partial credit model.

Description

Usage

Arguments

Format