summary.PREM

Provides a summary of a PREM model, as returned by <code>Bayes_PREM()</code>.

Provides a set of models to estimate nonlinear longitudinal data using Bayesian estimation methods. These models include the: 1) Bayesian Piecewise Random Effects Model (Bayes_PREM()) which estimates a piecewise random effects (mixture) model for a given number of latent classes and a latent number of possible changepoints in each class, and can incorporate class and outcome predictive covariates (see Lamm (2022) <https://hdl.handle.net/11299/252533> and Lock et al., (2018) <doi:10.1007/s11336-017-9594-5>), 2) Bayesian Crossed Random Effects Model (Bayes_CREM()) which estimates a linear, quadratic, exponential, or piecewise crossed random effects models where individuals are changing groups over time (e.g., students and schools; see Rohloff et al., (2024) <doi:10.1111/bmsp.12334>), and 3) Bayesian Bivariate Piecewise Random Effects Model (Bayes_BPREM()) which estimates a bivariate piecewise random effects model to jointly model two related outcomes (e.g., reading and math achievement; see Peralta et al., (2022) <doi:10.1037/met0000358>).

Corissa T. Rohloff

BEND

Bayesian Estimation of Nonlinear Data (BEND)

Rik Lamm

Yadira Peralta

Nidhi Kohli

Eric F. Lock

summary.PREM function

<dl><dt>object</dt>
<dd>An object of class "PREM" (returned by <code>Bayes_PREM(...)</code>).</dd>
<dt>...</dt>
<dd>Additional arguments.</dd></dl>

Arguments

Author

Summarize the results of a piecewise random effects model (PREM) — summary.PREM

<dl>

<dt>object</dt>
<dd>An object of class "PREM" (returned by <code>Bayes_PREM(...)</code>).</dd>


<dt>...</dt>
<dd>Additional arguments.</dd>

</dl>

summary.PREM: Summarize the results of a piecewise random effects model (PREM)

Description

Usage

Value

Arguments

Author

Examples