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

Back to User's Guide Examples