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Chapter 7: Mixture Modeling with Cross-Sectional Data

Download all Chapter 7 examples

Example View output Download input Download data View Monte Carlo output Download Monte Carlo input
7.1: Mixture regression analysis for a continuous dependent variable using automatic starting values with random starts ex7.1 ex7.1.inp ex7.1.dat mcex7.1 mcex7.1.inp
7.2: Mixture regression analysis for a count variable using a zero-inflated Poisson model using automatic starting values with random starts ex7.2 ex7.2.inp ex7.2.dat mcex7.2 mcex7.2.inp
7.3: LCA with binary latent class indicators using automatic starting values with random starts ex7.3 ex7.3.inp ex7.3.dat mcex7.3 mcex7.3.inp
7.4: LCA with binary latent class indicators using user-specified starting values without random starts ex7.4 ex7.4.inp ex7.4.dat mcex7.4 mcex7.4.inp
7.5: LCA with binary latent class indicators using user-specified starting values with random starts ex7.5 ex7.5.inp ex7.5.dat mcex7.5 mcex7.5.inp
7.6: LCA with three-category latent class indicators using user-specified starting values without random starts ex7.6 ex7.6.inp ex7.6.dat mcex7.6 mcex7.6.inp
7.7: LCA with unordered categorical latent class indicators using automatic starting values with random starts ex7.7 ex7.7.inp ex7.7.dat mcex7.7 mcex7.7.inp
7.8: LCA with unordered categorical latent class indicators using user-specified starting values with random starts ex7.8 ex7.8.inp ex7.8.dat mcex7.8 mcex7.8.inp
7.9: LCA with continuous latent class indicators using automatic starting values with random starts ex7.9 ex7.9.inp ex7.9.dat mcex7.9 mcex7.9.inp
7.10: LCA with continuous latent class indicators using user-specified starting values without random starts ex7.10 ex7.10.inp ex7.10.dat mcex7.10 mcex7.10.inp
7.11: LCA with binary, censored, unordered, and count latent class indicators using user-specified starting values without random starts ex7.11 ex7.11.inp ex7.11.dat mcex7.11 mcex7.11.inp
7.12: LCA with binary latent class indicators using automatic starting values with random starts with a covariate and a direct effect ex7.12 ex7.12.inp ex7.12.dat mcex7.12 mcex7.12.inp
7.13: Confirmatory LCA with binary latent class indicators and parameter constraints ex7.13 ex7.13.inp ex7.13.dat mcex7.13 mcex7.13.inp
7.14: Confirmatory LCA with two categorical latent variables ex7.14 ex7.14.inp ex7.14.dat mcex7.14 mcex7.14.inp
7.15: Loglinear model for a three-way table with conditional independence between the first two variables ex7.15 ex7.15.inp ex7.15.dat mcex7.15 mcex7.15.inp
7.16: LCA with partial conditional independence ex7.16 ex7.16.inp ex7.16.dat mcex7.16 mcex7.16.inp
7.17: Mixture CFA modeling ex7.17 ex7.17.inp ex7.17.dat mcex7.17 mcex7.17.inp
7.18: LCA with a second-order factor (twin analysis) ex7.18 ex7.18.inp ex7.18.dat mcex7.18 mcex7.18.inp
7.19: SEM with a categorical latent variable regressed on a continuous latent variable ex7.19 ex7.19.inp ex7.19.dat mcex7.19 mcex7.19.inp
7.20: Structural equation mixture modeling ex7.20 ex7.20.inp ex7.20.dat mcex7.20 mcex7.20.inp
7.21: Mixture modeling with known classes (multiple group analysis) ex7.21 ex7.21.inp ex7.21.dat mcex7.21 mcex7.21.inp
7.22: Mixture modeling with continuous variables that correlate within class ex7.22 ex7.22.inp ex7.22.dat mcex7.22 mcex7.22.inp
7.23: Mixture randomized trials modeling using CACE estimation with training data (data for this example cannot be created with Monte Carlo so only the input is provided) none ex7.23.inp none none none
7.24: Mixture randomized trials modeling using CACE estimation with missing data on the latent class indicator ex7.24 ex7.24.inp ex7.24.dat mcex7.24 mcex7.24.inp
7.25: Zero-inflated Poisson regression carried out as a two-class model ex7.25 ex7.25.inp ex7.25.dat mcex7.25 mcex7.25.inp
7.26: CFA with a non-parametric representation of a non-normal factor distribution ex7.26 ex7.26.inp ex7.26.dat mcex7.26 mcex7.26.inp
7.27: Factor (IRT) mixture analysis with binary latent class and factor indicators ex7.27 ex7.27.inp ex7.27.dat mcex7.27 mcex7.27.inp
7.28: Two-group twin model for categorical outcomes using maximum likelihood and parameter constraints ex7.28 ex7.28.inp ex7.28.dat mcex7.28 mcex7.28.inp
7.29: Two-group IRT twin model for factors with categorical factor indicators using parameter constraints ex7.29 ex7.29.inp ex7.29.dat mcex7.29 mcex7.29.inp
7.30: Continuous-time survival analysis using a Cox regression model to estimate a treatment effect ex7.30 ex7.30.inp ex7.30.dat mcex7.30 mcex7.30.inp

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