SimData_PREM

<p>Simulated data for a piecewise random effects model (PREM) and useful extensions (CI-PREM, PREMM, CI-PREMM) with 18 timepoints collected on 30 individuals.</p>

datasets

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

SimData_PREM function

<p>A data frame with 540 rows and 6 variables.</p>

Format

Simulated data for a PREM + Extensions — SimData_PREM

Simulated data for a PREM + Extensions

SimData_PREM: Simulated data for a PREM + Extensions

Description

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