MPLUS Analysis Examples Replication Chapter 10
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1 MPLUS Analysis Examples Replication Chapter 10 Mplus includes all input code and output in the *.out file. This document contains selected output from each analysis for Chapter 10. All data preparation and management was done using SAS and then read into Mplus using a text file format produced by SAS. Some options available in Stata or other software presented in Chapter 10 including Kaplan-Meier curves and the Clog-log logistic model for comparison to the logit model are not available in Mplus. As a result, they not included in this document. 1
2 !NOTE: Mplus provides parameter estimates that can be transformed to Hazard Ratios by using the exponent of the beta. Confidence intervals can also be calculated manually. Mplus VERSION /02/2017 3:26 PM INPUT INSTRUCTIONS TITLE: ASDA 2 EXAMPLE NCSR DATA SURVIVAL ANALYSIS COX MODEL! USE CHAPTER 10 DATA SET PREPARED IN SAS DATA: FILE IS "P:\ASDA 2\Data sets\ncsr\ncsr_c10_mplus.txt"; VARIABLE: NAMES ARE AGE CASEID DSM_SO ED4CAT MAR3CAT MDE_OND NCSRWTLG NCSRWTSH OBESE6CA REGION SECLUSTR SESTRAT SEX SO_OND WKSTAT3C ag60 ag1829 ag3044 ag4559 ag4cat ageonsetmde ald black ed011 ed12 ed16 ed1315 hisp intwage married mde ncsrwtsh100 nevmar numsecu other prevmar racecat sexf sexm white ; USEVARIABLES ARE sestrat numsecu ncsrwtsh ageonsetmde mde age sexm prevmar nevmar ed12 ed1315 ed16 hisp black white ; missing are. ; WEIGHT IS NCSRWTsh ; stratification is sestrat ; cluster is numsecu ; survival=ageonsetmde (all) ; timecensored=mde (0=right 1=not) ; ANALYSIS: type is complex; Model: ageonsetmde on age sexm prevmar nevmar ed12 ed1315 ed16 hisp black white ; *** WARNING in VARIABLE command Note that only the first 8 characters of variable names are used in the output. Shorten variable names to avoid any confusion. 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS ASDA 2 EXAMPLE 10.1 NCSR DATA SURVIVAL ANALYSIS COX MODEL SUMMARY OF ANALYSIS Number of groups 1 Number of observations 9282 Number of dependent variables 1 Number of independent variables 10 Number of continuous latent variables 0 Observed dependent variables Time-to-event (survival) Non-parametric AGEONSET Observed independent variables AGE SEXM PREVMAR NEVMAR ED12 ED1315 ED16 HISP BLACK WHITE Variables with special functions Stratification Cluster variable Weight variable SESTRAT NUMSECU NCSRWTSH Time-censoring variables 2
3 MDE Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 100 Convergence criterion Optimization Specifications for the EM Algorithm Maximum number of iterations 500 Convergence criteria Loglikelihood change Relative loglikelihood change Derivative Categorical Latent variables Censored, Binary or Ordered Categorical (Ordinal), Unordered Categorical (Nominal) and Count Outcomes Maximum value for logit thresholds 15 Minimum value for logit thresholds -15 Minimum expected cell size for chi-square 0.100D-01 Maximum number of iterations for H Convergence criterion for H D-03 Optimization algorithm EMA Integration Specifications Type STANDARD Number of integration points 15 Dimensions of numerical integration 0 Adaptive quadrature ON Base Hazard OFF Cholesky OFF Input data file(s) P:\ASDA 2\Data sets\ncsr\ncsr_c10_mplus.txt Input data format FREE SUMMARY OF DATA Number of strata 42 Number of clusters 84 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 10 Loglikelihood H0 Value H0 Scaling Correction Factor for MLR Information Criteria Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC (n* = (n + 2) / 24) 3
4 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value AGEONSETMD ON AGE SEXM PREVMAR NEVMAR ED ED ED HISP BLACK WHITE QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix (ratio of smallest to largest eigenvalue) 0.870E-04 CONFIDENCE INTERVALS OF MODEL RESULTS Lower.5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper.5% AGEONSET ON AGE SEXM PREVMAR NEVMAR ED ED ED HISP BLACK WHITE DIAGRAM INFORMATION Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram. If running Mplus from the Mplus Diagrammer, the diagram opens automatically. Diagram output p:\asda 2\analysis example replication\mplus\chapter 9\asda 2 section cox model.dgm Beginning Time: 15:26:47 Ending Time: 15:26:49 Elapsed Time: 00:00: Stoner Ave. Los Angeles, CA Tel: (310) Fax: (310) Web: Support: Support@StatModel.com Copyright (c) Muthen & Muthen 4
5 Mplus VERSION /03/2017 8:42 AM INPUT INSTRUCTIONS TITLE: ASDA 2 EXAMPLE 10.2 DISCRETE TIME LOGISTIC MODEL! USE CHAPTER 10 DISCRETE TIME DATA SET PREPARED IN SAS DATA: FILE IS "P:\ASDA 2\Data sets\ncsr\ncsr_c10dt_mplus.txt"; VARIABLE: NAMES ARE AGE CASEID DSM_SO ED4CAT MAR3CAT MDE_OND NCSRWTLG NCSRWTSH OBESE6CA REGION SECLUSTR SESTRAT SEX SO_OND WKSTAT3C ag60 ag1829 ag3044 ag4559 ag4cat ageonsetmde ald black ed011 ed12 ed16 ed1315 hisp intwage married mde mdetv ncsrwtsh100 nevmar numsecu other prevmar pyr racecat sexf sexm white ; USEVARIABLES ARE numsecu mdetv pyr age sexm ed12 ed1315 ed16 hisp black white prevmar nevmar ageonsetmde ; missing are. ; WEIGHT IS ncsrwtsh ; stratification is sestrat ; cluster is numsecu ; SUBPOPULATION =(PYR <= AGEONSETMDE) ; categorical = mdetv ; ANALYSIS: type is complex; estimator=mlr ; Model: mdetv on pyr age sexm ed12 ed1315 ed16 hisp black white prevmar nevmar ; *** WARNING in VARIABLE command Note that only the first 8 characters of variable names are used in the output. Shorten variable names to avoid any confusion. *** WARNING in MODEL command Variable is uncorrelated with all other variables: AGEONSETMDE *** WARNING in MODEL command At least one variable is uncorrelated with all other variables in the model. Check that this is what is intended. 3 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS ASDA 2 EXAMPLE 10.2 DISCRETE TIME LOGISTIC MODEL SUMMARY OF ANALYSIS Number of groups 1 Number of observations Number of dependent variables 2 Number of independent variables 11 Number of continuous latent variables 0 Observed dependent variables Continuous AGEONSETMD Binary and ordered categorical (ordinal) MDETV Observed independent variables PYR AGE SEXM ED12 ED1315 ED16 HISP BLACK WHITE PREVMAR NEVMAR Variables with special functions Stratification SESTRAT 5
6 Cluster variable Weight variable NUMSECU NCSRWTSH Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 100 Convergence criterion Optimization Specifications for the EM Algorithm Maximum number of iterations 500 Convergence criteria Loglikelihood change Relative loglikelihood change Derivative Categorical Latent variables Censored, Binary or Ordered Categorical (Ordinal), Unordered Categorical (Nominal) and Count Outcomes Maximum value for logit thresholds 15 Minimum value for logit thresholds -15 Minimum expected cell size for chi-square 0.100D-01 Maximum number of iterations for H Convergence criterion for H D-03 Optimization algorithm EMA Integration Specifications Type STANDARD Number of integration points 15 Dimensions of numerical integration 0 Adaptive quadrature ON Link LOGIT Cholesky OFF Input data file(s) P:\ASDA 2\Data sets\ncsr\ncsr_c10dt_mplus.txt Input data format FREE SUMMARY OF DATA Number of missing data patterns 1 Number of y missing data patterns 1 Number of u missing data patterns 1 Number of strata 42 Number of clusters 84 UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES MDETV Category Category THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 14 Loglikelihood H0 Value H0 Scaling Correction Factor for MLR 6
7 Information Criteria Akaike (AIC) Bayesian (BIC) Sample-Size Adjusted BIC (n* = (n + 2) / 24) MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value MDETV ON PYR AGE SEXM ED ED ED HISP BLACK WHITE PREVMAR NEVMAR Means AGEONSETMD Thresholds MDETV$ Variances AGEONSETMD LOGISTIC REGRESSION ODDS RATIO RESULTS MDETV ON PYR AGE SEXM ED ED ED HISP BLACK WHITE PREVMAR NEVMAR QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix (ratio of smallest to largest eigenvalue) 0.412E Stoner Ave. Los Angeles, CA Tel: (310) Fax: (310) Web: Support: Support@StatModel.com Copyright (c) Muthen & Muthen 7
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