Mplus VERSION 7.4
MUTHEN & MUTHEN
03/17/2016 12:03 PM
INPUT INSTRUCTIONS
TITLE:
path analysis with a binary outcome and a continuous
mediator with missing: Monte Carlo integration
DATA:
FILE = lsaydropout.dat;
VARIABLE:
NAMES = lsayid female mothed homeres expect math7 math8
math9 math10 lunch expel arrest droptht7 hisp black hsdrop
md710 urban tracking ntrack mothsei race mth11 mth12
totstud lchpart african hispan asian self worth other
satisf respect failure esteem problem cloctn dloctn eloctn
floctn gloctn hloctn iloctn jloctn kloctn lloctn drop7
drop8 drop9 drop10 drop11 drop12 cloc dloc eloc floc
gloc hloc iloc jloc kloc lloc dopout misdrop miss7
miss8 miss9 miss10 pat1 pat2 pat3 pat4 pat5 pat6 pat7
pat8 pat9 pat10 pat11 pat12 pat13 pat14 pat15 pat16 drop
dof12 dos11 dof11 dos10 dof10 dos9 dof9 dos8 dof8 cprob1
cprob2 cprob3 class expect7 expect8 expect9 expect10
expect11 expect12 dropot7 dropot8 dropot9 dropot10
dropot11 dropot12;
!pat1-16== missing data pattern dummy
!lloc2==dropout status at 12th grade (0=not dropout, 1=dropout)
!expect7-12==college expectations in 7-12th grade
!dropt7-12==thoughts of dropout in 7-12th grade
USEV = math7 math10;
IDVARIABLE = lsayid;
MISSING = ALL(9999);
USEOBS = (hloc == 0 OR iloc == 0 OR jloc == 0 OR kloc == 0
OR lloc == 0) AND math7 NE 9999;
ANALYSIS:
ESTIMATOR = bayes;
chains = 1;
fbiter = 200;
MODEL:
math7 WITH math10;
OUTPUT:
patterns standardized tech1 tech8;
plot:
type = plot2;
savedata:
bparameters = bpars.dat;
INPUT READING TERMINATED NORMALLY
path analysis with a binary outcome and a continuous
mediator with missing: Monte Carlo integration
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 2898
Number of dependent variables 2
Number of independent variables 0
Number of continuous latent variables 0
Observed dependent variables
Continuous
MATH7 MATH10
Variables with special functions
ID variable LSAYID
Estimator BAYES
Specifications for Bayesian Estimation
Point estimate MEDIAN
Number of Markov chain Monte Carlo (MCMC) chains 1
Random seed for the first chain 0
Starting value information UNPERTURBED
Treatment of categorical mediator LATENT
Algorithm used for Markov chain Monte Carlo GIBBS(PX1)
Fixed number of iterations 200
K-th iteration used for thinning 1
Input data file(s)
lsaydropout.dat
Input data format FREE
SUMMARY OF DATA
SUMMARY OF MISSING DATA PATTERNS
Number of missing data patterns 2
MISSING DATA PATTERNS (x = not missing)
1 2
MATH7 x x
MATH10 x
MISSING DATA PATTERN FREQUENCIES
Pattern Frequency Pattern Frequency
1 2009 2 889
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT
Covariance Coverage
MATH7 MATH10
________ ________
MATH7 1.000
MATH10 0.693 0.693
THE MODEL ESTIMATION TERMINATED NORMALLY
USE THE FBITERATIONS OPTION TO INCREASE THE NUMBER OF ITERATIONS BY A FACTOR
OF AT LEAST TWO TO CHECK CONVERGENCE AND THAT THE PSR VALUE DOES NOT INCREASE.
MODEL FIT INFORMATION
Number of Free Parameters 5
Bayesian Posterior Predictive Checking using Chi-Square
95% Confidence Interval for the Difference Between
the Observed and the Replicated Chi-Square Values
-3.517 7.661
Posterior Predictive P-Value 0.455
Information Criteria
Deviance (DIC) 35910.924
Estimated Number of Parameters (pD) 5.411
Bayesian (BIC) 35940.110
MODEL RESULTS
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
MATH7 WITH
MATH10 107.331 3.700 0.000 98.563 113.590 *
Means
MATH7 50.911 0.178 0.000 50.578 51.290 *
MATH10 63.026 0.260 0.000 62.627 63.548 *
Variances
MATH7 102.477 3.003 0.000 95.585 106.892 *
MATH10 183.972 5.692 0.000 170.334 193.649 *
STANDARDIZED MODEL RESULTS
STDYX Standardization
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
MATH7 WITH
MATH10 0.783 0.009 0.000 0.765 0.797 *
Means
MATH7 5.033 0.077 0.000 4.907 5.207 *
MATH10 4.644 0.075 0.000 4.518 4.827 *
Variances
MATH7 1.000 0.000 0.000 1.000 1.000
MATH10 1.000 0.000 0.000 1.000 1.000
STDY Standardization
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
MATH7 WITH
MATH10 0.783 0.009 0.000 0.765 0.797 *
Means
MATH7 5.033 0.077 0.000 4.907 5.207 *
MATH10 4.644 0.075 0.000 4.518 4.827 *
Variances
MATH7 1.000 0.000 0.000 1.000 1.000
MATH10 1.000 0.000 0.000 1.000 1.000
STD Standardization
Posterior One-Tailed 95% C.I.
Estimate S.D. P-Value Lower 2.5% Upper 2.5% Significance
MATH7 WITH
MATH10 0.783 0.009 0.000 0.765 0.797 *
Means
MATH7 50.911 0.178 0.000 50.578 51.290 *
MATH10 63.026 0.260 0.000 62.627 63.548 *
Variances
MATH7 102.477 3.003 0.000 95.585 106.892 *
MATH10 183.972 5.692 0.000 170.334 193.649 *
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION
NU
MATH7 MATH10
________ ________
1 1 2
THETA
MATH7 MATH10
________ ________
MATH7 3
MATH10 4 5
STARTING VALUES
NU
MATH7 MATH10
________ ________
1 50.882 63.713
THETA
MATH7 MATH10
________ ________
MATH7 51.330
MATH10 0.000 92.492
PRIORS FOR ALL PARAMETERS PRIOR MEAN PRIOR VARIANCE PRIOR STD. DEV.
Parameter 1~N(0.000,infinity) 0.0000 infinity infinity
Parameter 2~N(0.000,infinity) 0.0000 infinity infinity
Parameter 3~IW(0.000,-3) infinity infinity infinity
Parameter 4~IW(0.000,-3) infinity infinity infinity
Parameter 5~IW(0.000,-3) infinity infinity infinity
TECHNICAL 8 OUTPUT
Simulated prior distributions
Parameter Prior Mean Prior Variance Prior Std. Dev.
Parameter 1 Improper Prior
Parameter 2 Improper Prior
Parameter 3 Improper Prior
Parameter 4 Improper Prior
Parameter 5 Improper Prior
TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION
POTENTIAL PARAMETER WITH
ITERATION SCALE REDUCTION HIGHEST PSR
100 1.065 2
200 1.077 2
PLOT INFORMATION
The following plots are available:
Bayesian posterior parameter distributions
Bayesian posterior parameter trace plots
Bayesian autocorrelation plots
Bayesian posterior predictive checking scatterplots
Bayesian posterior predictive checking distribution plots
SAVEDATA INFORMATION
Bayesian Parameters
Save file
bpars.dat
Save format Free
Order of parameters saved
Parameter 1, [ MATH7 ]
Parameter 2, [ MATH10 ]
Parameter 3, MATH7
Parameter 4, MATH10 WITH MATH7
Parameter 5, MATH10
STD, [ MATH7 ]
STD, [ MATH10 ]
STD, MATH7
STD, MATH10 WITH MATH7
STD, MATH10
STDYX, [ MATH7 ]
STDYX, [ MATH10 ]
STDYX, MATH7
STDYX, MATH10 WITH MATH7
STDYX, MATH10
STDY, [ MATH7 ]
STDY, [ MATH10 ]
STDY, MATH7
STDY, MATH10 WITH MATH7
STDY, MATH10
R-SQUARE, MATH7
R-SQUARE, MATH10
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
c:\users\bengt 2013\documents\bengt\mplus runs\a book - topic 1 mplus runs\bayes\bivar lsay intr
Beginning Time: 12:03:39
Ending Time: 12:03:40
Elapsed Time: 00:00:01
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