Proc Traj

A SAS Procedure Based on Mixture Models for Estimating Developmental Trajectories. This article introduces a new SAS procedure written by the authors that analyzes longitudinal data (developmental trajectories) by fitting a mixture model. The TRAJ procedure fits semiparametric (discrete) mixtures of censored normal, Poisson, zero-inflated Poisson, and Bernoulli distributions to longitudinal data. Applications to psychometric scale data, offense counts, and a dichotomous prevalence measure in violence research are illustrated. In addition, the use of the Bayesian information criterion to address the problem of model selection, including the estimation of the number of components in the mixture, is demonstrated.


References in zbMATH (referenced in 12 articles )

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  1. Heggeseth, Brianna C.; Jewell, Nicholas P.: How Gaussian mixture models might miss detecting factors that impact growth patterns (2018)
  2. Nummi, Tapio; Salonen, Janne; Koskinen, Lasse; Pan, Jianxin: A semiparametric mixture regression model for longitudinal data (2018)
  3. McNeish, Daniel; Harring, Jeffrey R.: The effect of model misspecification on growth mixture model class enumeration (2017)
  4. Heylen, Joke; van Mechelen, Iven; Verduyn, Philippe; Ceulemans, Eva: KSC-N: clustering of hierarchical time profile data (2016)
  5. Francis, Brian; Liu, Jiayi: Modelling escalation in crime seriousness: a latent variable approach (2015)
  6. Chu, Man-Kee M.; Koval, John J.: Trajectory modeling of longitudinal binary data: application of the EM algorithm for mixture models (2014)
  7. Codd, Casey L.; Cudeck, Robert: Nonlinear random-effects mixture models for repeated measures (2014)
  8. Nielsen, J. D.; Rosenthal, J. S.; Sun, Y.; Day, D. M.; Bevc, I.; Duchesne, T.: Group-based criminal trajectory analysis using cross-validation criteria (2014)
  9. Montagna, Silvia; Tokdar, Surya T.; Neelon, Brian; Dunson, David B.: Bayesian latent factor regression for functional and longitudinal data (2012)
  10. Genolini, Christophe; Falissard, Bruno: KmL: k-means for longitudinal data (2010)
  11. Wang, Jichuan: Longitudinal study on growth trajectory of crack-cocaine use: applications of group-based trajectory models (2010)
  12. Haviland, Amelia M.; Nagin, Daniel S.: Causal inferences with group based trajectory models (2005)