Recursive Partitioning with Nonlinear Models of Change

Stegmann, Gabriela; Jacobucci, Ross; Serang, Sarfaraz; Grimm, Kevin J.

doi:10.1080/00273171.2018.1461602

Cited by 16 publications

(16 citation statements)

References 41 publications

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“…The LIP yields the percent improvement in the −2LL from the baseline model for the more parameterized model. The same metric, but scaled in terms of the proportional improvement in model fit, has been implemented in recursive partitioning algorithms for mixed-effects (Abdolell et al, 2002;Stegmann et al, 2018) and structural equation models (Serang et al, 2020). The main reason to do this is to provide context regarding the relative improvement in model fit.…”

Section: An Effect Size Measure For Model Comparisonmentioning

confidence: 99%

Model Fit and Comparison in Finite Mixture Models: A Review and a Novel Approach

2021

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One of the greatest challenges in the application of finite mixture models is model comparison. A variety of statistical fit indices exist, including information criteria, approximate likelihood ratio tests, and resampling techniques; however, none of these indices describe the amount of improvement in model fit when a latent class is added to the model. We review these model fit statistics and propose a novel approach, the likelihood increment percentage per parameter (LIPpp), targeting the relative improvement in model fit when a class is added to the model. Simulation work based on two previous simulation studies highlighted the potential for the LIPpp to identify the correct number of classes, and provide context for the magnitude of improvement in model fit. We conclude with recommendations and future research directions.

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Section: An Effect Size Measure For Model Comparisonmentioning

confidence: 99%

Model Fit and Comparison in Finite Mixture Models: A Review and a Novel Approach

2021

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“…It should be noted that there are other algorithms and software packages that allow for recursive partitioning of GLMM-type models, such as SEM trees (Brandmaier et al, 2013), longRpart (Abdolell et al, 2002) and longRpart2 (Stegmann et al, 2018). In the current paper, we focus on GLMM trees, because it allows for partitioning based on variables measured at both the lowest level (e.g., patient level) as well as higher levels (e.g., therapist, treatment centre, region level).…”

Section: Unbiased Recursive Partitioning and Extension To Multilevel And Longitudinal Datamentioning

confidence: 99%

Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data

Fokkema

Edbrooke‐Childs

Wolpert

2020

Psychotherapy Research

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Objective: Decision-tree methods are machine-learning methods which provide results that are relatively easy to interpret and apply by human decision makers. The resulting decision trees show how baseline patient characteristics can be combined to predict treatment outcomes for individual patients, for example. This paper introduces GLMM trees, a decision-tree method for multilevel and longitudinal data.Method: To illustrate, we apply GLMM trees to a dataset of 3,256 young people (mean age 11.33, 48% girls) receiving treatment at one of several mental-health service providers in the UK. Two treatment outcomes (mental-health difficulties scores corrected for baseline) were regressed on 18 demographic, case and severity characteristics at baseline. We compared the performance of GLMM trees with that of traditional GLMMs and random forests.Results: GLMM trees yielded modest predictive accuracy, with cross-validated multiple R values of .18 and .25. Predictive accuracy did not differ significantly from that of traditional GLMMs and random forests, while GLMM trees required evaluation of a lower number of variables.Conclusion: GLMM trees provide a useful data-analytic tool for clinical prediction problems. The supplemental material provides a tutorial for replicating the GLMM tree analyses in R.

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“…For example, Austin (2012) compare inferences on the effect of in-hospital smoking cessation counselling on subsequent mortality in patients hospitalized with an acute myocardial infarction using ensemble-based methods (bagged regression trees, random forests, and boosted regression trees) to directly estimate average treatment effects by imputing potential outcomes. Stegmann et al (2018) introduce nonlinear Longitudinal Recursive Partitioning (nLRP) and illustrate its use with empirical data from the kindergarten cohort of the Early Childhood Longitudinal Study. Or again, recently, Grimm and Jacobucci (2020) improve the reliability of splitting functions in the case of small data samples, and illustrate their performance using data on depression and suicidal ideation from the National Longitudinal Survey of Youth.…”

Section: Introductionmentioning

confidence: 99%

Comparing Boosting and Bagging for Decision Trees of Rankings

et al. 2021

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Decision tree learning is among the most popular and most traditional families of machine learning algorithms. While these techniques excel in being quite intuitive and interpretable, they also suffer from instability: small perturbations in the training data may result in big changes in the predictions. The so-called ensemble methods combine the output of multiple trees, which makes the decision more reliable and stable. They have been primarily applied to numeric prediction problems and to classification tasks. In the last years, some attempts to extend the ensemble methods to ordinal data can be found in the literature, but no concrete methodology has been provided for preference data. In this paper, we extend decision trees, and in the following also ensemble methods to ranking data. In particular, we propose a theoretical and computational definition of bagging and boosting, two of the best known ensemble methods. In an experimental study using simulated data and real-world datasets, our results confirm that known results from classification, such as that boosting outperforms bagging, could be successfully carried over to the ranking case.

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Recursive Partitioning with Nonlinear Models of Change

Cited by 16 publications

References 41 publications

Model Fit and Comparison in Finite Mixture Models: A Review and a Novel Approach

Model Fit and Comparison in Finite Mixture Models: A Review and a Novel Approach

Generalized linear mixed-model (GLMM) trees: A flexible decision-tree method for multilevel and longitudinal data

Comparing Boosting and Bagging for Decision Trees of Rankings

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