A Lasso and a Regression Tree Mixed-Effect Model with Random Effects for the Level, the Residual Variance, and the Autocorrelation

Abstract

Research in psychology is experiencing a rapid increase in the availability of intensive longitudinal data. To use such data for predicting feelings, beliefs, and behavior, recent methodological work suggested combinations of the longitudinal mixed-effect model with Lasso regression or with regression trees. The present article adds to this literature by suggesting an extension of these models that—in addition to a random effect for the mean level—also includes a random effect for the within-subject variance and a random effect for the autocorrelation. After introducing the extended mixed-effect location scale (E-MELS), the extended mixed-effect location-scale Lasso model (Lasso E-MELS), and the extended mixed-effect location-scale tree model (E-MELS trees), we show how its parameters can be estimated using a marginal maximum likelihood approach. Using real and simulated example data, we illustrate how to use E-MELS, Lasso E-MELS, and E-MELS trees for building prediction models to forecast individuals’ daily nervousness. The article is accompanied by an R package (called ) and functions that support users in the application of the suggested models.

Daily Dynamics of Grandiose Narcissism: Distribution, Stability, and Trait Relations of Admiration and Rivalry States and State Contingencies

“…3 More information about the FLUX and FLIP sample data can be found in Nestler and Hofmann (2020). The FLUX and FLIP data have also been used elsewhere (Nestler, 2020a(Nestler, , 2020b(Nestler, , 2020cNestler & Humberg, 2020). 4 To analyze the stability of interindividual differences in distribution parameters and in state contingencies, the data were split into four blocks of 14 days for Sample 1 and into two blocks of 42 days for Sample 2.…”

Daily dynamics of grandiose narcissism: distribution, stability, and trait relations of admiration and rivalry states and state contingencies

“…3More information about the FLUX and FLIP sample data can be found in Nestler and Hofmann (2020). The FLUX and FLIP data have also been used elsewhere (Nestler, 2020a; 2020b; 2020c; Nestler & Humberg, 2020). …”

Responsible research assessment in the area of methodological or quantitative research: A comment on Gärtner et al. (2022)

“…Besides many others, current research areas include machine learning based approaches for RESPONSIBLE QUANTITATIVE METHODS RESEARCH 4 social sciences that are interpretable (Henninger, Debelak, Rothacher, & Strobl, 2022), Bayesian modeling and model selection (Heck et al, 2022), or new approaches for intensive longitudinal or other complex data (e.g., Nestler & Humberg, 2022;Orzek & Voelkle, 2023).…”