Population stochastic modelling (PSM)—An R package for mixed-effects models based on stochastic differential equations

2009 ◽  
Vol 94 (3) ◽  
pp. 279-289 ◽  
Author(s):  
Søren Klim ◽  
Stig Bousgaard Mortensen ◽  
Niels Rode Kristensen ◽  
Rune Viig Overgaard ◽  
Henrik Madsen
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Stochastic Modelling ◽  
R Package ◽  
Mixed Effects ◽  
Mixed Effects Models

scholarly journals Understanding mixed effects models through data simulation

2019 ◽  
Author(s):  
Lisa Marie DeBruine ◽  
Dale J. Barr
Keyword(s):  
Random Effects ◽  
R Package ◽  
Mixed Effects ◽  
Experimental Designs ◽  
Mixed Effects Models ◽  
Data Simulation ◽  
Linear Mixed Effects ◽  
Power Calculations

Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of both subjects and stimuli using mixed effects models. However, much of this research is analyzed using ANOVA on aggregated responses because researchers are not confident specifying and interpreting mixed effects models. The tutorial will explain how to simulate data with random effects structure and analyse the data using linear mixed effects regression (with the lme4 R package), with a focus on interpreting the output in light of the simulated parameters. Data simulation can not only enhance understanding of how these models work, but also enables researchers to perform power calculations for complex designs. All materials associated with this article can be accessed at https://osf.io/3cz2e/.

scholarly journals Understanding Mixed-Effects Models Through Data Simulation

2021 ◽  
Vol 4 (1) ◽  
pp. 251524592096511
Author(s):  
Lisa M. DeBruine ◽  
Dale J. Barr
Keyword(s):  
Random Effects ◽  
Analysis Of Variance ◽  
R Package ◽  
Mixed Effects ◽  
Experimental Designs ◽  
Mixed Effects Models ◽  
Data Simulation ◽  
Linear Mixed Effects ◽  
Power Calculations

Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of both subjects and stimuli using mixed-effects models. However, much of this research is analyzed using analysis of variance on aggregated responses because researchers are not confident specifying and interpreting mixed-effects models. This Tutorial explains how to simulate data with random-effects structure and analyze the data using linear mixed-effects regression (with the lme4 R package), with a focus on interpreting the output in light of the simulated parameters. Data simulation not only can enhance understanding of how these models work, but also enables researchers to perform power calculations for complex designs. All materials associated with this article can be accessed at https://osf.io/3cz2e/ .

Sign in / Sign up

Export Citation Format

Share Document