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Chapter 8: Mixture Modeling with Longitudinal Data

Download all Chapter 8 examples

Example View output Download input Download data View Monte Carlo output Download Monte Carlo input
8.1: GMM for a continuous outcome using automatic starting values and random starts ex8.1 ex8.1.inp ex8.1.dat mcex8.1 mcex8.1.inp
8.2: GMM for a continuous outcome using user-specified starting values and random starts ex8.2 ex8.2.inp ex8.2.dat mcex8.2 mcex8.2.inp
8.3: GMM for a censored outcome using a censored model with automatic starting values and random starts ex8.3 ex8.3.inp ex8.3.dat mcex8.3 mcex8.3.inp
8.4: GMM for a categorical outcome using automatic starting values and random starts ex8.4 ex8.4.inp ex8.4.dat mcex8.4 mcex8.4.inp
8.5: GMM for a count outcome using a zero-inflated Poisson model and a negative binomial model with automatic starting values and random starts (part 1) ex8.5part1 ex8.5part1.inp ex8.5a.dat mcex8.5part1 mcex8.5part1.inp
8.5: GMM for a count outcome using a zero-inflated Poisson model and a negative binomial model with automatic starting values and random starts (part 2) ex8.5part2 ex8.5part2.inp ex8.5b.dat mcex8.5part2 mcex8.5part2.inp
8.6: GMM with a categorical distal outcome using automatic starting values and random starts ex8.6 ex8.6.inp ex8.6.dat mcex8.6 mcex8.6.inp
8.7: A sequential process GMM for continuous outcomes with two categorical latent variables ex8.7 ex8.7.inp ex8.7.dat mcex8.7 mcex8.7.inp
8.8: GMM with known classes (multiple group analysis) ex8.8 ex8.8.inp ex8.8.dat mcex8.8 mcex8.8.inp
8.9: LCGA for a binary outcome ex8.9 ex8.9.inp ex8.9.dat mcex8.9 mcex8.9.inp
8.10: LCGA for a three-category outcome ex8.10 ex8.10.inp ex8.10.dat mcex8.10 mcex8.10.inp
8.11: LCGA for a count outcome using a zero-inflated Poisson model ex8.11 ex8.11.inp ex8.11.dat mcex8.11 mcex8.11.inp
8.12: Hidden Markov model with four time points ex8.12 ex8.12.inp ex8.12.dat mcex8.12 mcex8.12.inp
8.13: LTA for two time points with a binary covariate influencing the latent transition probabilities (part 1) ex8.13part1 ex8.13part1.inp ex8.13.dat mcex8.13 mcex8.13.inp
8.13: LTA for two time points with a binary covariate influencing the latent transition probabilities (part 2) ex8.13part2 ex8.13part2.inp ex8.13.dat N/A N/A
8.14: LTA for two time points with a continuous covariate influencing the latent transition probabilities ex8.14 ex8.14.inp ex8.14.dat mcex8.14 mcex8.14.inp
8.15: Mover-stayer LTA for three time points using a probability parameterization ex8.15 ex8.15.inp ex8.15.dat mcex8.15 mcex8.15.inp
8.16: Discrete-time survival mixture analysis with survival predicted by growth trajectory classes (data for this example cannot be created with Monte Carlo so only the input is provided) N/A ex8.16.inp N/A N/A N/A
8.17: Continuous-time survival mixture analysis using a Cox regression model ex8.17 ex8.17.inp ex8.17.dat mcex8.17 mcex8.17.inp

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