```Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010  11:01 PM

INPUT INSTRUCTIONS

Title:  app13

Recid2

Recidivism Example

Discrete time survival analysis in mixture modeling framework

Event is first arrest after prison release

Time scale is 4 week intervals, numbered 1 - 13

Hazard function is unstructured

Finaid included with time-invariant effects --> proportionality assumed

One-class model

VARIABLES:
u1-u13 = I(first arrest, months 1-13), censored=999
finaid = 1 if financial aid was provided, 0 if no aid (INTERVENTION)
age = age at release (in years)
race = 1 if black, 0 if white
wexp = 1 if prior work experience, 0 if no work experience
mar = 1 if married, 0 if unmarried
parole = 1 if paroled, 0 if not paroled
priors = number of prior arrests
educ = years of schooling
empb1-empb13 = I(one of more weeks of employment during interval)
tr1-tr2 = Training data for long-term survivor class

Data:

File is recid.dat;

Variable:

Names are id u1-u13 finaid age race wexp mar parole
priors educ empb1-empb13 tr1 tr2;

Missing are all (999);

Usevariables are u1-u13 finaid;

Categorical are u1-u13;

Classes = c(1);

Analysis:

Type = Mixture Missing;
MIterations = 1000;
MConvergence = 0.000001;
LogCriterion = 0.0000001;
Convergence = 0.000001;

Model:

%overall%

f by u1-u13@1;

f on finaid;

[f@0];

%c#1%

[u1\$1*4.7 u2\$1*4.0 u3\$1*4.1 u4\$1*3.9 u5\$1*3.4 u6\$1*3.9 u7\$1*3.6];
[u8\$1*4.3 u9\$1*3.5 u10\$1*3.5 u11\$1*3.7 u12\$1*3.6 u13\$1*3.3];

f on finaid*;

[f@0];

Output:

Tech1;
Tech8;

*** WARNING in ANALYSIS command
Starting with Version 5, TYPE=MISSING is the default for all analyses.
To obtain listwise deletion, use LISTWISE=ON in the DATA command.
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS

app13

Recid2

Recidivism Example

Discrete time survival analysis in mixture modeling framework

Event is first arrest after prison release

Time scale is 4 week intervals, numbered 1 - 13

Hazard function is unstructured

Finaid included with time-invariant effects --> proportionality assumed

One-class model

VARIABLES:
u1-u13 = I(first arrest, months 1-13), censored=999
finaid = 1 if financial aid was provided, 0 if no aid (INTERVENTION)
age = age at release (in years)
race = 1 if black, 0 if white
wexp = 1 if prior work experience, 0 if no work experience
mar = 1 if married, 0 if unmarried
parole = 1 if paroled, 0 if not paroled
priors = number of prior arrests
educ = years of schooling
empb1-empb13 = I(one of more weeks of employment during interval)
tr1-tr2 = Training data for long-term survivor class

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         432

Number of dependent variables                                   13
Number of independent variables                                  1
Number of continuous latent variables                            1
Number of categorical latent variables                           1

Observed dependent variables

Binary and ordered categorical (ordinal)
U1          U2          U3          U4          U5          U6
U7          U8          U9          U10         U11         U12
U13

Observed independent variables
FINAID

Continuous latent variables
F

Categorical latent variables
C

Estimator                                                      MLR
Information matrix                                        OBSERVED
Optimization Specifications for the Quasi-Newton Algorithm for
Continuous Outcomes
Maximum number of iterations                                 100
Convergence criterion                                  0.100D-05
Optimization Specifications for the EM Algorithm
Maximum number of iterations                                1000
Convergence criteria
Loglikelihood change                                 0.100D-06
Relative loglikelihood change                        0.100D-06
Derivative                                           0.100D-05
Optimization Specifications for the M step of the EM Algorithm for
Categorical Latent variables
Number of M step iterations                                    1
M step convergence criterion                           0.100D-05
Basis for M step termination                           ITERATION
Optimization Specifications for the M step of the EM Algorithm for
Censored, Binary or Ordered Categorical (Ordinal), Unordered
Categorical (Nominal) and Count Outcomes
Number of M step iterations                                    1
M step convergence criterion                           0.100D-05
Basis for M step termination                           ITERATION
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 H1                           2000
Convergence criterion for H1                             0.100D-03
Optimization algorithm                                         EMA

Input data file(s)
recid.dat
Input data format  FREE

SUMMARY OF DATA

Number of missing data patterns            13
Number of y missing data patterns           0
Number of u missing data patterns          13

COVARIANCE COVERAGE OF DATA

Minimum covariance coverage value   0.100

PROPORTION OF DATA PRESENT FOR U

Covariance Coverage
U1            U2            U3            U4            U5
________      ________      ________      ________      ________
U1             1.000
U2             0.991         0.991
U3             0.972         0.972         0.972
U4             0.956         0.956         0.956         0.956
U5             0.938         0.938         0.938         0.938         0.938
U6             0.907         0.907         0.907         0.907         0.907
U7             0.889         0.889         0.889         0.889         0.889
U8             0.866         0.866         0.866         0.866         0.866
U9             0.854         0.854         0.854         0.854         0.854
U10            0.829         0.829         0.829         0.829         0.829
U11            0.803         0.803         0.803         0.803         0.803
U12            0.785         0.785         0.785         0.785         0.785
U13            0.764         0.764         0.764         0.764         0.764

Covariance Coverage
U6            U7            U8            U9            U10
________      ________      ________      ________      ________
U6             0.907
U7             0.889         0.889
U8             0.866         0.866         0.866
U9             0.854         0.854         0.854         0.854
U10            0.829         0.829         0.829         0.829         0.829
U11            0.803         0.803         0.803         0.803         0.803
U12            0.785         0.785         0.785         0.785         0.785
U13            0.764         0.764         0.764         0.764         0.764

Covariance Coverage
U11           U12           U13
________      ________      ________
U11            0.803
U12            0.785         0.785
U13            0.764         0.764         0.764

UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

U1
Category 1    0.991      428.000
Category 2    0.009        4.000
U2
Category 1    0.981      420.000
Category 2    0.019        8.000
U3
Category 1    0.983      413.000
Category 2    0.017        7.000
U4
Category 1    0.981      405.000
Category 2    0.019        8.000
U5
Category 1    0.968      392.000
Category 2    0.032       13.000
U6
Category 1    0.980      384.000
Category 2    0.020        8.000
U7
Category 1    0.974      374.000
Category 2    0.026       10.000
U8
Category 1    0.987      369.000
Category 2    0.013        5.000
U9
Category 1    0.970      358.000
Category 2    0.030       11.000
U10
Category 1    0.969      347.000
Category 2    0.031       11.000
U11
Category 1    0.977      339.000
Category 2    0.023        8.000
U12
Category 1    0.973      330.000
Category 2    0.027        9.000
U13
Category 1    0.964      318.000
Category 2    0.036       12.000

THE MODEL ESTIMATION TERMINATED NORMALLY

TESTS OF MODEL FIT

Loglikelihood

H0 Value                        -534.835
H0 Scaling Correction Factor       1.000
for MLR

Information Criteria

Number of Free Parameters             14
Akaike (AIC)                    1097.670
Bayesian (BIC)                  1154.628
(n* = (n + 2) / 24)

FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL

Latent
Classes

1        432.00000          1.00000

FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES

Latent
Classes

1        432.00000          1.00000

CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

Latent
Classes

1              432          1.00000

Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)

1

1   1.000

MODEL RESULTS

Two-Tailed
Estimate       S.E.  Est./S.E.    P-Value

Latent Class 1

F        BY
U1                 1.000      0.000    999.000    999.000
U2                 1.000      0.000    999.000    999.000
U3                 1.000      0.000    999.000    999.000
U4                 1.000      0.000    999.000    999.000
U5                 1.000      0.000    999.000    999.000
U6                 1.000      0.000    999.000    999.000
U7                 1.000      0.000    999.000    999.000
U8                 1.000      0.000    999.000    999.000
U9                 1.000      0.000    999.000    999.000
U10                1.000      0.000    999.000    999.000
U11                1.000      0.000    999.000    999.000
U12                1.000      0.000    999.000    999.000
U13                1.000      0.000    999.000    999.000

F          ON
FINAID            -0.374      0.192     -1.948      0.051

Intercepts
F                  0.000      0.000    999.000    999.000

Thresholds
U1\$1               4.503      0.520      8.662      0.000
U2\$1               3.789      0.363     10.450      0.000
U3\$1               3.906      0.379     10.316      0.000
U4\$1               3.754      0.362     10.360      0.000
U5\$1               3.235      0.291     11.135      0.000
U6\$1               3.700      0.367     10.079      0.000
U7\$1               3.449      0.346      9.978      0.000
U8\$1               4.124      0.459      8.992      0.000
U9\$1               3.305      0.306     10.809      0.000
U10\$1              3.276      0.319     10.261      0.000
U11\$1              3.569      0.373      9.578      0.000
U12\$1              3.422      0.344      9.934      0.000
U13\$1              3.097      0.307     10.102      0.000

QUALITY OF NUMERICAL RESULTS

Condition Number for the Information Matrix              0.714E-01
(ratio of smallest to largest eigenvalue)

TECHNICAL 1 OUTPUT

PARAMETER SPECIFICATION FOR LATENT CLASS 1

NU
FINAID
________
1                  0

LAMBDA
FINAID
________
FINAID             0

THETA
FINAID
________
FINAID             0

ALPHA
FINAID
________
1                  0

BETA
FINAID
________
FINAID             0

PSI
FINAID
________
FINAID             0

PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART

TAU(U) FOR LATENT CLASS 1
U1\$1          U2\$1          U3\$1          U4\$1          U5\$1
________      ________      ________      ________      ________
1                  1             2             3             4             5

TAU(U) FOR LATENT CLASS 1
U6\$1          U7\$1          U8\$1          U9\$1          U10\$1
________      ________      ________      ________      ________
1                  6             7             8             9            10

TAU(U) FOR LATENT CLASS 1
U11\$1         U12\$1         U13\$1
________      ________      ________
1                 11            12            13

PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART

ALPHA(C)
C#1
________
1                  0

GAMMA(C)
FINAID
________
C#1                0

PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR GROWTH MODEL PART

LAMBDA(F) FOR LATENT CLASS 1
F
________
U1                 0
U2                 0
U3                 0
U4                 0
U5                 0
U6                 0
U7                 0
U8                 0
U9                 0
U10                0
U11                0
U12                0
U13                0

ALPHA(F) FOR LATENT CLASS 1
F
________
1                  0

GAMMA(F) FOR LATENT CLASS 1
FINAID
________
F                 14

STARTING VALUES FOR LATENT CLASS 1

NU
FINAID
________
1              0.000

LAMBDA
FINAID
________
FINAID         1.000

THETA
FINAID
________
FINAID         0.000

ALPHA
FINAID
________
1              0.000

BETA
FINAID
________
FINAID         0.000

PSI
FINAID
________
FINAID         0.125

STARTING VALUES FOR LATENT CLASS INDICATOR MODEL PART

TAU(U) FOR LATENT CLASS 1
U1\$1          U2\$1          U3\$1          U4\$1          U5\$1
________      ________      ________      ________      ________
1              4.700         4.000         4.100         3.900         3.400

TAU(U) FOR LATENT CLASS 1
U6\$1          U7\$1          U8\$1          U9\$1          U10\$1
________      ________      ________      ________      ________
1              3.900         3.600         4.300         3.500         3.500

TAU(U) FOR LATENT CLASS 1
U11\$1         U12\$1         U13\$1
________      ________      ________
1              3.700         3.600         3.300

STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART

ALPHA(C)
C#1
________
1              0.000

GAMMA(C)
FINAID
________
C#1            0.000

STARTING VALUES FOR LATENT CLASS INDICATOR GROWTH MODEL PART

LAMBDA(F) FOR CLASS LATENT CLASS 1
F
________
U1             1.000
U2             1.000
U3             1.000
U4             1.000
U5             1.000
U6             1.000
U7             1.000
U8             1.000
U9             1.000
U10            1.000
U11            1.000
U12            1.000
U13            1.000

ALPHA(F) FOR LATENT CLASS 1
F
________
1              0.000

GAMMA(F) FOR LATENT CLASS 1
FINAID
________
F              0.000

TECHNICAL 8 OUTPUT

ITER  LOGLIKELIHOOD    ABS CHANGE   REL CHANGE      CLASS COUNTS    ALGORITHM
1 -0.53679672D+03    0.0000000    0.0000000    432.000              EM
2 -0.53485113D+03    1.9455911    0.0036244    432.000              EM
3 -0.53483514D+03    0.0159898    0.0000299    432.000              EM
4 -0.53483514D+03    0.0000010    0.0000000    432.000              EM
5 -0.53483514D+03    0.0000000    0.0000000    432.000              EM

Beginning Time:  23:01:08
Ending Time:  23:01:08
Elapsed Time:  00:00:00

MUTHEN & MUTHEN
3463 Stoner Ave.
Los Angeles, CA  90066

Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com

Copyright (c) 1998-2010 Muthen & Muthen
```