Chapter 9: Descriptive Statistics for Time-Invariant Variables

The MEANS Procedure

Variable Label N Mean Std Dev Minimum Maximum
Attitude12
PMmonitor
Monitor18
Attitude12: Attitudes at Age 12
PMmonitor: Person Mean Monitoring
Monitor18: Monitoring at Age18 Occasion
200
200
200
3.9505004
3.0754391
3.0676727
0.6024956
0.5526113
0.5561235
2.4373666
1.1535011
1.3397759
5.0000000
4.4729399
4.4207726


Chapter 9: Descriptive Statistics for Time-Invariant Variables

The CORR Procedure

3 Variables: PMmonitor Monitor12 Monitor18

Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
PMmonitor 200 3.07544 0.55261 615.08782 1.15350 4.47294 PMmonitor: Person Mean Monitoring
Monitor12 200 3.07831 0.80403 615.66149 1.00000 5.00000 Monitor12: Monitoring at Age12 Occasion
Monitor18 200 3.06767 0.55612 613.53454 1.33978 4.42077 Monitor18: Monitoring at Age18 Occasion

Pearson Correlation Coefficients, N = 200
Prob > |r| under H0: Rho=0
  PMmonitor Monitor12 Monitor18
PMmonitor
PMmonitor: Person Mean Monitoring
1.00000
 
0.91371
<.0001
0.74007
<.0001
Monitor12
Monitor12: Monitoring at Age12 Occasion
0.91371
<.0001
1.00000
 
0.52464
<.0001
Monitor18
Monitor18: Monitoring at Age18 Occasion
0.74007
<.0001
0.52464
<.0001
1.00000
 


Chapter 9: Descriptive Statistics for Time-Varying Variables

The MEANS Procedure

Variable Label N Mean Std Dev Minimum Maximum
age
risky
monitor
WPmon
Change18mon
age: Exact Age at Occasion
risky: Risky Behavior at Occasion
monitor: Monitoring at Occasion
WPmon: Within-Person Monitoring (0=PM)
Change18mon: WP Monitoring from Age 18 (0=Age18)
1400
1400
1400
1400
1400
15.0010317
19.3848852
3.0754391
3.172066E-18
0.0077664
2.0041741
5.3013587
0.6445657
0.3337595
0.5200831
11.5308790
10.0000000
1.0000000
-1.1360535
-2.3552837
18.3368796
36.2824265
5.0000000
1.2261286
1.7131926


The CORR Procedure

4 Variables: PMmon3 Age18mon3 WPmon Change18mon

Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
PMmon3 1400 0.07544 0.55143 105.61474 -1.84650 1.47294 PMmon3: Person Mean Monitoring (0=3)
Age18mon3 1400 0.06767 0.55493 94.74177 -1.66022 1.42077 Age18mon3: BP Monitoring at Age 18 (0=3)
WPmon 1400 0 0.33376 0 -1.13605 1.22613 WPmon: Within-Person Monitoring (0=PM)
Change18mon 1400 0.00777 0.52008 10.87298 -2.35528 1.71319 Change18mon: WP Monitoring from Age 18 (0=Age18)

Pearson Correlation Coefficients, N = 1400
Prob > |r| under H0: Rho=0
  PMmon3 Age18mon3 WPmon Change18mon
PMmon3
PMmon3: Person Mean Monitoring (0=3)
1.00000
 
0.74007
<.0001
0.00000
1.0000
0.27061
<.0001
Age18mon3
Age18mon3: BP Monitoring at Age 18 (0=3)
0.74007
<.0001
1.00000
 
0.00000
1.0000
-0.28233
<.0001
WPmon
WPmon: Within-Person Monitoring (0=PM)
0.00000
1.0000
0.00000
1.0000
1.00000
 
0.64174
<.0001
Change18mon
Change18mon: WP Monitoring from Age 18 (0=Age18)
0.27061
<.0001
-0.28233
<.0001
0.64174
<.0001
1.00000
 


Ch 9: Empty Means, Random Intercept Model for Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable monitor
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 2
Columns in X 1
Columns in Z per Subject 1
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 2742.32779338  
1 1 1674.48750256 0.00000000

Convergence criteria met.

Estimated V Matrix for PersonID 1
Row Col1 Col2 Col3 Col4 Col5 Col6 Col7
1 0.4152 0.2853 0.2853 0.2853 0.2853 0.2853 0.2853
2 0.2853 0.4152 0.2853 0.2853 0.2853 0.2853 0.2853
3 0.2853 0.2853 0.4152 0.2853 0.2853 0.2853 0.2853
4 0.2853 0.2853 0.2853 0.4152 0.2853 0.2853 0.2853
5 0.2853 0.2853 0.2853 0.2853 0.4152 0.2853 0.2853
6 0.2853 0.2853 0.2853 0.2853 0.2853 0.4152 0.2853
7 0.2853 0.2853 0.2853 0.2853 0.2853 0.2853 0.4152

Estimated V Correlation Matrix for PersonID 1
Row Col1 Col2 Col3 Col4 Col5 Col6 Col7
1 1.0000 0.6872 0.6872 0.6872 0.6872 0.6872 0.6872
2 0.6872 1.0000 0.6872 0.6872 0.6872 0.6872 0.6872
3 0.6872 0.6872 1.0000 0.6872 0.6872 0.6872 0.6872
4 0.6872 0.6872 0.6872 1.0000 0.6872 0.6872 0.6872
5 0.6872 0.6872 0.6872 0.6872 1.0000 0.6872 0.6872
6 0.6872 0.6872 0.6872 0.6872 0.6872 1.0000 0.6872
7 0.6872 0.6872 0.6872 0.6872 0.6872 0.6872 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr > Z
UN(1,1) PersonID 0.2853 0.03039 9.39 <.0001
Residual   0.1299 0.005302 24.49 <.0001

Fit Statistics
-2 Log Likelihood 1674.5
AIC (Smaller is Better) 1680.5
AICC (Smaller is Better) 1680.5
BIC (Smaller is Better) 1690.4

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 1067.84 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1674.5 3 1680.5 1680.5 1684.5 1690.4 1693.4

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 3.0754 0.03898 200 78.90 <.0001 0.05 2.9986 3.1523


Ch 9: Fixed Linear Age, Random Intercept Model for Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable monitor
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 2
Columns in X 2
Columns in Z per Subject 1
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 2742.08696224  
1 2 1674.03679975 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr > Z
UN(1,1) PersonID 0.2853 0.03039 9.39 <.0001
Residual   0.1298 0.005300 24.49 <.0001

Fit Statistics
-2 Log Likelihood 1674.0
AIC (Smaller is Better) 1682.0
AICC (Smaller is Better) 1682.1
BIC (Smaller is Better) 1695.2

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 1068.05 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1674.0 4 1682.0 1682.1 1687.4 1695.2 1699.2

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 3.0658 0.04156 258 73.77 <.0001 0.05 2.9839 3.1476
agec18 -0.00323 0.004809 1200 -0.67 0.5021 0.05 -0.01266 0.006206

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1200 0.45 0.45 0.5020 0.5021


PsuedoR2 (% Reduction) for CovEmpty vs. CovFixLin

Name CovParm Subject Estimate StdErr ZValue ProbZ PseudoR2
CovEmpty UN(1,1) PersonID 0.2853 0.03039 9.39 <.0001 .
CovEmpty Residual   0.1299 0.005302 24.49 <.0001 .
CovFixLin UN(1,1) PersonID 0.2853 0.03039 9.39 <.0001 .000072442
CovFixLin Residual   0.1298 0.005300 24.49 <.0001 .000360515


Ch 9: Random Linear Age Model for Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable monitor
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 2
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 2742.08696224  
1 2 1353.75224380 0.00000002
2 1 1353.75222857 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 0.1954 0.02333 8.38 <.0001
UN(2,1) PersonID -0.00042 0.004008 -0.11 0.9159
UN(2,2) PersonID 0.01049 0.001345 7.80 <.0001
Residual   0.08075 0.003612 22.36 <.0001

Fit Statistics
-2 Log Likelihood 1353.8
AIC (Smaller is Better) 1365.8
AICC (Smaller is Better) 1365.8
BIC (Smaller is Better) 1385.5

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 1388.33 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1353.8 6 1365.8 1365.8 1373.8 1385.5 1391.5

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 3.0650 0.03413 200 89.81 <.0001 0.05 2.9977 3.1323
agec18 -0.00331 0.008179 200 -0.40 0.6865 0.05 -0.01943 0.01282

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 200 0.16 0.16 0.6861 0.6865


Likelihood Ratio Test for FitFixLin vs. FitRandLin

Name Neg2LogLike Parms AIC BIC DevDiff DFdiff Pvalue
FitFixLin 1674.0 4 1682.0 1695.2 . . .
FitRandLin 1353.8 6 1365.8 1385.5 320.285 2 0


Ch 9: Fixed Quadratic, Random Linear Age Model for Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable monitor
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 3
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 2742.02002457  
1 2 1353.73247073 0.00000003
2 1 1353.73245397 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 0.1954 0.02333 8.38 <.0001
UN(2,1) PersonID -0.00042 0.004008 -0.11 0.9157
UN(2,2) PersonID 0.01049 0.001345 7.80 <.0001
Residual   0.08075 0.003612 22.36 <.0001

Fit Statistics
-2 Log Likelihood 1353.7
AIC (Smaller is Better) 1367.7
AICC (Smaller is Better) 1367.8
BIC (Smaller is Better) 1390.8

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 1388.29 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1353.7 7 1367.7 1367.8 1377.1 1390.8 1397.8

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 3.0635 0.03579 242 85.59 <.0001 0.05 2.9930 3.1340
agec18 -0.00513 0.01533 1089 -0.33 0.7380 0.05 -0.03521 0.02495
agec18*agec18 -0.00030 0.002161 1008 -0.14 0.8882 0.05 -0.00454 0.003937

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1089 0.11 0.11 0.7379 0.7380
agec18*agec18 1 1008 0.02 0.02 0.8882 0.8882


PsuedoR2 (% Reduction) for CovRandLin vs. CovFixQuad

Name CovParm Subject Estimate StdErr ZValue ProbZ PseudoR2
CovRandLin UN(1,1) PersonID 0.1954 0.02333 8.38 <.0001 .
CovRandLin UN(2,2) PersonID 0.01049 0.001345 7.80 <.0001 .
CovRandLin Residual   0.08075 0.003612 22.36 <.0001 .
CovFixQuad UN(1,1) PersonID 0.1954 0.02333 8.38 <.0001 -.000008682
CovFixQuad UN(2,2) PersonID 0.01049 0.001345 7.80 <.0001 -.000029165
CovFixQuad Residual   0.08075 0.003612 22.36 <.0001 0.000025539


Ch 9: Random Quadratic Age Model for Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable monitor
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 7
Columns in X 3
Columns in Z per Subject 3
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 2742.02002457  
1 4 1361.23250924 0.03341252
2 2 1348.93443095 0.00135669
3 1 1348.04715416 0.00002087
4 1 1348.03420078 0.00000001

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 0.2197 0.02747 8.00 <.0001
UN(2,1) PersonID 0.01520 0.007978 1.91 0.0567
UN(2,2) PersonID 0.01253 0.003417 3.67 0.0001
UN(3,1) PersonID 0.002464 0.001038 2.37 0.0176
UN(3,2) PersonID 0.000168 0.000254 0.66 0.5075
UN(3,3) PersonID 0 . . .
Residual   0.08076 0.003612 22.36 <.0001

Fit Statistics
-2 Log Likelihood 1348.0
AIC (Smaller is Better) 1366.0
AICC (Smaller is Better) 1366.2
BIC (Smaller is Better) 1395.7

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
5 1393.99 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1348.0 9 1366.0 1366.2 1378.0 1395.7 1404.7

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 3.0627 0.03745 207 81.78 <.0001 0.05 2.9888 3.1365
agec18 -0.00602 0.01566 339 -0.38 0.7010 0.05 -0.03682 0.02479
agec18*agec18 -0.00047 0.002161 1008 -0.22 0.8294 0.05 -0.00471 0.003774

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 339 0.15 0.15 0.7008 0.7010
agec18*agec18 1 1008 0.05 0.05 0.8293 0.8294


Likelihood Ratio Test for FitFixQuad vs. FitRandQuad

Name Neg2LogLike Parms AIC BIC DevDiff DFdiff Pvalue
FitFixQuad 1353.7 7 1367.7 1390.8 . . .
FitRandQuad 1348.0 9 1366.0 1395.7 5.69825 2 0.057895


Ch 9: Fixed Quadratic, Random Linear Age Model for Risky Behavior
Conditional Baseline with Attitudes Predicting Linear Age Slope

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 5
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8265.86865661  
1 2 7602.53277799 0.00000001

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 18.0772 2.2040 8.20 <.0001
UN(2,1) PersonID 1.8838 0.3563 5.29 <.0001
UN(2,2) PersonID 0.4878 0.07976 6.12 <.0001
Residual   8.3535 0.3736 22.36 <.0001

Fit Statistics
-2 Log Likelihood 7602.5
AIC (Smaller is Better) 7620.5
AICC (Smaller is Better) 7620.7
BIC (Smaller is Better) 7650.2

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 663.34 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7602.5 9 7620.5 7620.7 7632.5 7650.2 7659.2

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.3120 0.3500 245 66.60 <.0001 0.05 22.6226 24.0015
agec18 1.9649 0.1460 1196 13.46 <.0001 0.05 1.6784 2.2514
agec18*agec18 0.1450 0.02196 1011 6.60 <.0001 0.05 0.1019 0.1881
att4 -3.1555 0.5512 200 -5.72 <.0001 0.05 -4.2425 -2.0685
agec18*att4 -0.5154 0.1043 199 -4.94 <.0001 0.05 -0.7211 -0.3098

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1196 181.09 181.09 <.0001 <.0001
agec18*agec18 1 1011 43.61 43.61 <.0001 <.0001
att4 1 200 32.77 32.77 <.0001 <.0001
agec18*att4 1 199 24.43 24.43 <.0001 <.0001


Eq 9.1: Predicting Quadratic Change in Risky Behavior
From Person Mean Monitoring as Between-Person Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 8
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8122.97621148  
1 2 7559.55190802 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 15.1849 1.9135 7.94 <.0001
UN(2,1) PersonID 1.7223 0.3317 5.19 <.0001
UN(2,2) PersonID 0.4792 0.07874 6.09 <.0001
Residual   8.2996 0.3712 22.36 <.0001

Fit Statistics
-2 Log Likelihood 7559.6
AIC (Smaller is Better) 7583.6
AICC (Smaller is Better) 7583.8
BIC (Smaller is Better) 7623.1

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 563.42 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7559.6 12 7583.6 7583.8 7599.6 7623.1 7635.1

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.4945 0.3312 252 70.93 <.0001 0.05 22.8421 24.1468
agec18 1.9256 0.1471 1197 13.09 <.0001 0.05 1.6370 2.2142
agec18*agec18 0.1362 0.02217 1011 6.14 <.0001 0.05 0.09271 0.1797
att4 -3.3195 0.5142 199 -6.46 <.0001 0.05 -4.3334 -2.3056
agec18*att4 -0.5238 0.1037 199 -5.05 <.0001 0.05 -0.7284 -0.3192
PMmon3 -2.5907 0.5924 248 -4.37 <.0001 0.05 -3.7575 -1.4239
agec18*PMmon3 0.4455 0.2618 1196 1.70 0.0891 0.05 -0.06824 0.9591
agec18*agec18*PMmon3 0.1037 0.03953 1009 2.62 0.0088 0.05 0.02613 0.1813

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1197 171.37 171.37 <.0001 <.0001
agec18*agec18 1 1011 37.75 37.75 <.0001 <.0001
att4 1 199 41.68 41.68 <.0001 <.0001
agec18*att4 1 199 25.49 25.49 <.0001 <.0001
PMmon3 1 248 19.12 19.12 <.0001 <.0001
agec18*PMmon3 1 1196 2.89 2.89 0.0889 0.0891
agec18*agec18*PMmon3 1 1009 6.88 6.88 0.0087 0.0088

Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Effect of PM Monitoring at Age 12 -1.5299 0.5449 264 -2.81 0.0054 0.05 -2.6028 -0.4569
Effect of PM Monitoring at Age 14 -2.7131 0.4343 234 -6.25 <.0001 0.05 -3.5687 -1.8576
Effect of PM Monitoring at Age 16 -3.0668 0.4559 231 -6.73 <.0001 0.05 -3.9650 -2.1685
Effect of PM Monitoring at Age 18 -2.5907 0.5924 248 -4.37 <.0001 0.05 -3.7575 -1.4239


PsuedoR2 (% Reduction) for CovAttOnly vs. CovPMBP

Name CovParm Subject Estimate StdErr ZValue ProbZ PseudoR2
CovAttOnly UN(1,1) PersonID 18.0772 2.2040 8.20 <.0001 .
CovAttOnly UN(2,2) PersonID 0.4878 0.07976 6.12 <.0001 .
CovAttOnly Residual   8.3535 0.3736 22.36 <.0001 .
CovPMBP UN(1,1) PersonID 15.1849 1.9135 7.94 <.0001 0.16000
CovPMBP UN(2,2) PersonID 0.4792 0.07874 6.09 <.0001 0.01756
CovPMBP Residual   8.2996 0.3712 22.36 <.0001 0.00645


Total R2 (% Reduction) for PredAttOnly vs. PredPMBP

Name PredCorr TotalR2 TotalR2Diff
PredAttOnly 0.48557 0.23578 .
PredPMBP 0.55670 0.30992 0.074143


Eq 9.1: Predicting Quadratic Change in Risky Behavior
From Monitoring at Age 18 as Between-Person Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 8
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8191.25566283  
1 2 7567.51793310 0.00000002
2 1 7567.51789338 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 16.1670 2.0092 8.05 <.0001
UN(2,1) PersonID 1.6884 0.3338 5.06 <.0001
UN(2,2) PersonID 0.4692 0.07748 6.06 <.0001
Residual   8.2263 0.3680 22.36 <.0001

Fit Statistics
-2 Log Likelihood 7567.5
AIC (Smaller is Better) 7591.5
AICC (Smaller is Better) 7591.7
BIC (Smaller is Better) 7631.1

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 623.74 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7567.5 12 7591.5 7591.7 7607.5 7631.1 7643.1

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.4147 0.3377 249 69.33 <.0001 0.05 22.7496 24.0799
agec18 1.9083 0.1461 1198 13.06 <.0001 0.05 1.6216 2.1950
agec18*agec18 0.1324 0.02204 1010 6.01 <.0001 0.05 0.08919 0.1757
att4 -3.3242 0.5274 199 -6.30 <.0001 0.05 -4.3642 -2.2843
agec18*att4 -0.5328 0.1030 199 -5.17 <.0001 0.05 -0.7359 -0.3296
Age18mon3 -1.7940 0.6037 248 -2.97 0.0033 0.05 -2.9831 -0.6050
agec18*Age18mon3 0.6552 0.2607 1197 2.51 0.0121 0.05 0.1438 1.1666
agec18*agec18*Age18mon3 0.1547 0.03927 1011 3.94 <.0001 0.05 0.07766 0.2318

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1198 170.55 170.55 <.0001 <.0001
agec18*agec18 1 1010 36.12 36.12 <.0001 <.0001
att4 1 199 39.73 39.73 <.0001 <.0001
agec18*att4 1 199 26.75 26.75 <.0001 <.0001
Age18mon3 1 248 8.83 8.83 0.0030 0.0033
agec18*Age18mon3 1 1197 6.32 6.32 0.0119 0.0121
agec18*agec18*Age18mon3 1 1011 15.52 15.52 <.0001 <.0001

Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Effect of Age 18 Monitoring at Age 12 -0.1551 0.5561 258 -0.28 0.7806 0.05 -1.2501 0.9400
Effect of Age 18 Monitoring at Age 14 -1.9392 0.4519 230 -4.29 <.0001 0.05 -2.8295 -1.0489
Effect of Age 18 Monitoring at Age 16 -2.4855 0.4718 228 -5.27 <.0001 0.05 -3.4152 -1.5558
Effect of Age 18 Monitoring at Age 18 -1.7940 0.6037 248 -2.97 0.0033 0.05 -2.9831 -0.6050


PsuedoR2 (% Reduction) for CovAttOnly vs. Cov18BP

Name CovParm Subject Estimate StdErr ZValue ProbZ PseudoR2
CovAttOnly UN(1,1) PersonID 18.0772 2.2040 8.20 <.0001 .
CovAttOnly UN(2,2) PersonID 0.4878 0.07976 6.12 <.0001 .
CovAttOnly Residual   8.3535 0.3736 22.36 <.0001 .
Cov18BP UN(1,1) PersonID 16.1670 2.0092 8.05 <.0001 0.10567
Cov18BP UN(2,2) PersonID 0.4692 0.07748 6.06 <.0001 0.03823
Cov18BP Residual   8.2263 0.3680 22.36 <.0001 0.01522


Total R2 (% Reduction) for PredAttOnly vs. Pred18BP

Name PredCorr TotalR2 TotalR2Diff
PredAttOnly 0.48557 0.23578 .
Pred18BP 0.52482 0.27543 0.039655


Eq 9.2: Adding Within-Person Monitoring by Quadratic Age
Using Deviation from Person Mean Monitoring as Within-Person Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 11
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8091.01918939  
1 2 7459.98141223 0.00001208
2 1 7459.95121903 0.00000001
3 1 7459.95118283 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 16.5791 2.0233 8.19 <.0001
UN(2,1) PersonID 2.1343 0.3716 5.74 <.0001
UN(2,2) PersonID 0.6109 0.09191 6.65 <.0001
Residual   7.3415 0.3309 22.18 <.0001

Fit Statistics
-2 Log Likelihood 7460.0
AIC (Smaller is Better) 7490.0
AICC (Smaller is Better) 7490.3
BIC (Smaller is Better) 7539.4

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 631.07 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7460.0 15 7490.0 7490.3 7510.0 7539.4 7554.4

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.4692 0.3365 240 69.74 <.0001 0.05 22.8063 24.1320
agec18 1.9209 0.1419 1170 13.53 <.0001 0.05 1.6424 2.1993
agec18*agec18 0.1365 0.02089 994 6.53 <.0001 0.05 0.09548 0.1775
att4 -3.3009 0.5270 197 -6.26 <.0001 0.05 -4.3401 -2.2616
agec18*att4 -0.5149 0.1101 185 -4.68 <.0001 0.05 -0.7322 -0.2977
PMmon3 -1.7308 0.6196 260 -2.79 0.0056 0.05 -2.9509 -0.5107
agec18*PMmon3 0.7579 0.2640 1187 2.87 0.0042 0.05 0.2399 1.2758
agec18*agec18*PMmon3 0.1219 0.03942 1014 3.09 0.0020 0.05 0.04453 0.1992
WPmon 2.5472 0.6085 1190 4.19 <.0001 0.05 1.3534 3.7410
agec18*WPmon -0.9523 0.4559 1226 -2.09 0.0369 0.05 -1.8467 -0.05780
agec18*agec18*WPmon -0.2159 0.07416 1233 -2.91 0.0037 0.05 -0.3614 -0.07038

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1170 183.13 183.13 <.0001 <.0001
agec18*agec18 1 994 42.68 42.68 <.0001 <.0001
att4 1 197 39.24 39.24 <.0001 <.0001
agec18*att4 1 185 21.87 21.87 <.0001 <.0001
PMmon3 1 260 7.80 7.80 0.0052 0.0056
agec18*PMmon3 1 1187 8.24 8.24 0.0041 0.0042
agec18*agec18*PMmon3 1 1014 9.56 9.56 0.0020 0.0020
WPmon 1 1190 17.52 17.52 <.0001 <.0001
agec18*WPmon 1 1226 4.36 4.36 0.0367 0.0369
agec18*agec18*WPmon 1 1233 8.47 8.47 0.0036 0.0037

Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Effect of PM Monitoring at Age 12 -1.8905 0.5825 293 -3.25 0.0013 0.05 -3.0369 -0.7441
Effect of PM Monitoring at Age 14 -2.8123 0.4351 233 -6.46 <.0001 0.05 -3.6695 -1.9551
Effect of PM Monitoring at Age 16 -2.7590 0.4579 231 -6.03 <.0001 0.05 -3.6612 -1.8569
Effect of PM Monitoring at Age 18 -1.7308 0.6196 260 -2.79 0.0056 0.05 -2.9509 -0.5107
Effect of WP Monitoring at Age 12 0.4892 0.6493 1169 0.75 0.4513 0.05 -0.7847 1.7631
Effect of WP Monitoring at Age 14 2.9022 0.3616 1069 8.03 <.0001 0.05 2.1927 3.6117
Effect of WP Monitoring at Age 16 3.5882 0.3694 1065 9.71 <.0001 0.05 2.8634 4.3130
Effect of WP Monitoring at Age 18 2.5472 0.6085 1190 4.19 <.0001 0.05 1.3534 3.7410


PsuedoR2 (% Reduction) for CovPMBP vs. CovPMBPWP

Name CovParm Subject Estimate StdErr ZValue ProbZ PseudoR2
CovPMBP UN(1,1) PersonID 15.1849 1.9135 7.94 <.0001 .
CovPMBP UN(2,2) PersonID 0.4792 0.07874 6.09 <.0001 .
CovPMBP Residual   8.2996 0.3712 22.36 <.0001 .
CovPMBPWP UN(1,1) PersonID 16.5791 2.0233 8.19 <.0001 -0.09181
CovPMBPWP UN(2,2) PersonID 0.6109 0.09191 6.65 <.0001 -0.27466
CovPMBPWP Residual   7.3415 0.3309 22.18 <.0001 0.11544


Total R2 (% Reduction) for PredPMBP vs. PredPMBPWP

Name PredCorr TotalR2 TotalR2Diff
PredPMBP 0.55670 0.30992 .
PredPMBPWP 0.56695 0.32143 0.011507


Eq 9.2: Adding Within-Person Monitoring by Quadratic Age
Using Change from Age 18 Monitoring as Within-Person Monitoring

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 11
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8170.16288261  
1 2 7504.69827961 0.00041938
2 1 7503.55077728 0.00001535
3 1 7503.51195725 0.00000003
4 1 7503.51189515 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 17.8434 2.1561 8.28 <.0001
UN(2,1) PersonID 1.7625 0.3470 5.08 <.0001
UN(2,2) PersonID 0.5177 0.08023 6.45 <.0001
Residual   7.4722 0.3394 22.01 <.0001

Fit Statistics
-2 Log Likelihood 7503.5
AIC (Smaller is Better) 7533.5
AICC (Smaller is Better) 7533.9
BIC (Smaller is Better) 7583.0

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 666.65 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7503.5 15 7533.5 7533.9 7553.5 7583.0 7598.0

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.4013 0.3461 238 67.62 <.0001 0.05 22.7196 24.0830
agec18 1.9428 0.1412 1178 13.76 <.0001 0.05 1.6658 2.2197
agec18*agec18 0.1382 0.02105 979 6.57 <.0001 0.05 0.09691 0.1795
att4 -3.3325 0.5446 196 -6.12 <.0001 0.05 -4.4065 -2.2584
agec18*att4 -0.5361 0.1045 197 -5.13 <.0001 0.05 -0.7422 -0.3301
Age18mon3 -1.4110 0.6225 242 -2.27 0.0243 0.05 -2.6371 -0.1848
agec18*Age18mon3 0.3412 0.2593 1190 1.32 0.1885 0.05 -0.1675 0.8499
agec18*agec18*Age18mon3 0.09308 0.03899 984 2.39 0.0172 0.05 0.01657 0.1696
Change18mon 4.7163 0.8615 1078 5.47 <.0001 0.05 3.0259 6.4067
agec18*Change18mon 1.1981 0.4730 1061 2.53 0.0114 0.05 0.2700 2.1262
agec18*agec18*Change18mon 0.09187 0.06070 1027 1.51 0.1305 0.05 -0.02724 0.2110

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1178 189.37 189.37 <.0001 <.0001
agec18*agec18 1 979 43.13 43.13 <.0001 <.0001
att4 1 196 37.44 37.44 <.0001 <.0001
agec18*att4 1 197 26.33 26.33 <.0001 <.0001
Age18mon3 1 242 5.14 5.14 0.0234 0.0243
agec18*Age18mon3 1 1190 1.73 1.73 0.1882 0.1885
agec18*agec18*Age18mon3 1 984 5.70 5.70 0.0170 0.0172
Change18mon 1 1078 29.97 29.97 <.0001 <.0001
agec18*Change18mon 1 1061 6.42 6.42 0.0113 0.0114
agec18*agec18*Change18mon 1 1027 2.29 2.29 0.1302 0.1305

Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Effect of Age 18 Monitoring at Age 12 -0.1070 0.5929 215 -0.18 0.8570 0.05 -1.2755 1.0616
Effect of Age 18 Monitoring at Age 14 -1.2863 0.4895 212 -2.63 0.0092 0.05 -2.2513 -0.3213
Effect of Age 18 Monitoring at Age 16 -1.7210 0.5037 232 -3.42 0.0007 0.05 -2.7134 -0.7285
Effect of Age 18 Monitoring at Age 18 -1.4110 0.6225 242 -2.27 0.0243 0.05 -2.6371 -0.1848
Effect of Change in Monitoring at Age 12 0.8346 0.3723 702 2.24 0.0253 0.05 0.1038 1.5655
Effect of Change in Monitoring at Age 14 1.3936 0.2974 1102 4.69 <.0001 0.05 0.8101 1.9771
Effect of Change in Monitoring at Age 16 2.6875 0.3235 1168 8.31 <.0001 0.05 2.0528 3.3222
Effect of Change in Monitoring at Age 18 4.7163 0.8615 1078 5.47 <.0001 0.05 3.0259 6.4067


PsuedoR2 (% Reduction) for Cov18BP vs. Cov18BPWP

Name CovParm Subject Estimate StdErr ZValue ProbZ PseudoR2
Cov18BP UN(1,1) PersonID 16.1670 2.0092 8.05 <.0001 .
Cov18BP UN(2,2) PersonID 0.4692 0.07748 6.06 <.0001 .
Cov18BP Residual   8.2263 0.3680 22.36 <.0001 .
Cov18BPWP UN(1,1) PersonID 17.8434 2.1561 8.28 <.0001 -0.10369
Cov18BPWP UN(2,2) PersonID 0.5177 0.08023 6.45 <.0001 -0.10352
Cov18BPWP Residual   7.4722 0.3394 22.01 <.0001 0.09167


Total R2 (% Reduction) for Pred18BP vs. Pred18BPWP

Name PredCorr TotalR2 TotalR2Diff
Pred18BP 0.52482 0.27543 .
Pred18BPWP 0.48940 0.23952 -0.035916


Ch 9 Eq 9.3: Multivariate Model of Risky Behavior and Monitoring
Tricking Univariate Software

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9DOUBLESTACK
Dependent Variable outcome
Covariance Structure Unstructured
Subject Effects PersonID, PersonID*occasion
Estimation Method ML
Residual Variance Method None
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 13
Columns in X 7
Columns in Z per Subject 4
Subjects 200
Max Obs per Subject 14

Number of Observations
Number of Observations Read 2800
Number of Observations Used 2800
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 14644.56135174  
1 2 8784.52736075 0.00000002
2 1 8784.52732156 0.00000000

Convergence criteria met.

Estimated R Matrix for PersonID*occasion
1 12
Row Col1 Col2
1 8.3538 0.2874
2 0.2874 0.08077

Estimated R Correlation Matrix
for PersonID*occasion 1 12
Row Col1 Col2
1 1.0000 0.3499
2 0.3499 1.0000

Estimated G Matrix
Row Effect PersonID:
Person ID
Number		 Col1 Col2 Col3 Col4
1 dvR 1 18.0644 -0.8554 1.8829 0.04072
2 dvM 1 -0.8554 0.1953 -0.1064 -0.00047
3 dvR*agec18 1 1.8829 -0.1064 0.4883 -0.01815
4 dvM*agec18 1 0.04072 -0.00047 -0.01815 0.01049

Estimated G Correlation Matrix
Row Effect PersonID:
Person ID
Number		 Col1 Col2 Col3 Col4
1 dvR 1 1.0000 -0.4554 0.6340 0.09356
2 dvM 1 -0.4554 1.0000 -0.3446 -0.01043
3 dvR*agec18 1 0.6340 -0.3446 1.0000 -0.2537
4 dvM*agec18 1 0.09356 -0.01043 -0.2537 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 18.0644 2.2040 8.20 <.0001
UN(2,1) PersonID -0.8554 0.1685 -5.08 <.0001
UN(2,2) PersonID 0.1953 0.02332 8.38 <.0001
UN(3,1) PersonID 1.8829 0.3564 5.28 <.0001
UN(3,2) PersonID -0.1064 0.03086 -3.45 0.0006
UN(3,3) PersonID 0.4883 0.07983 6.12 <.0001
UN(4,1) PersonID 0.04072 0.03879 1.05 0.2939
UN(4,2) PersonID -0.00047 0.004005 -0.12 0.9062
UN(4,3) PersonID -0.01815 0.007344 -2.47 0.0135
UN(4,4) PersonID 0.01049 0.001344 7.80 <.0001
UN(1,1) PersonID*occasion 8.3538 0.3737 22.36 <.0001
UN(2,1) PersonID*occasion 0.2874 0.02753 10.44 <.0001
UN(2,2) PersonID*occasion 0.08077 0.003613 22.35 <.0001

Fit Statistics
-2 Log Likelihood 8784.5
AIC (Smaller is Better) 8824.5
AICC (Smaller is Better) 8824.8
BIC (Smaller is Better) 8890.5

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
12 5860.03 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
8784.5 20 8824.5 8824.8 8851.2 8890.5 8910.5

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
dvR 23.3138 0.3477 239 67.06 <.0001 0.05 22.6289 23.9988
dvM 3.0650 0.03412 200 89.83 <.0001 0.05 2.9978 3.1323
dvR*agec18 1.9743 0.1386 1185 14.25 <.0001 0.05 1.7024 2.2461
dvM*agec18 -0.00328 0.008176 200 -0.40 0.6884 0.05 -0.01941 0.01284
dvR*agec18*agec18 0.1466 0.02058 1010 7.12 <.0001 0.05 0.1062 0.1869
dvR*att4 -3.3328 0.5126 199 -6.50 <.0001 0.05 -4.3436 -2.3220
dvR*agec18*att4 -0.5298 0.1025 199 -5.17 <.0001 0.05 -0.7319 -0.3276

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
dvR 1 239 4496.37 4496.37 <.0001 <.0001
dvM 1 200 8069.47 8069.47 <.0001 <.0001
dvR*agec18 1 1185 202.98 202.98 <.0001 <.0001
dvM*agec18 1 200 0.16 0.16 0.6879 0.6884
dvR*agec18*agec18 1 1010 50.71 50.71 <.0001 <.0001
dvR*att4 1 199 42.28 42.28 <.0001 <.0001
dvR*agec18*att4 1 199 26.70 26.70 <.0001 <.0001


Ch 9: Random Linear Age Model for Monitoring
Saving Predicted Random Effects and Residuals as Data

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable monitor
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 2
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 2742.08696224  
1 2 1353.75224380 0.00000002
2 1 1353.75222857 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 0.1954 0.02333 8.38 <.0001
UN(2,1) PersonID -0.00042 0.004008 -0.11 0.9159
UN(2,2) PersonID 0.01049 0.001345 7.80 <.0001
Residual   0.08075 0.003612 22.36 <.0001

Fit Statistics
-2 Log Likelihood 1353.8
AIC (Smaller is Better) 1365.8
AICC (Smaller is Better) 1365.8
BIC (Smaller is Better) 1385.5

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 1388.33 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1353.8 6 1365.8 1365.8 1373.8 1385.5 1391.5

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 3.0650 0.03413 200 89.81 <.0001 0.05 2.9977 3.1323
agec18 -0.00331 0.008179 200 -0.40 0.6865 0.05 -0.01943 0.01282

Solution for Random Effects
Effect PersonID:
Person ID
Number		 Estimate Std Err Pred DF t Value Pr > |t|
Intercept 1 -0.2618 0.1669 924 -1.57 0.1170
agec18 1 0.03944 0.04549 843 0.87 0.3861
Intercept 2 0.4919 0.1717 889 2.86 0.0043
agec18 2 -0.08408 0.04531 823 -1.86 0.0639
Intercept 3 0.4498 0.1712 857 2.63 0.0088
agec18 3 -0.2221 0.04751 771 -4.68 <.0001
Intercept 4 0.2308 0.1733 844 1.33 0.1833
agec18 4 0.007761 0.04736 765 0.16 0.8699
Intercept 5 0.4876 0.1704 887 2.86 0.0043
agec18 5 0.04639 0.04608 810 1.01 0.3144
Intercept 6 0.2076 0.1638 968 1.27 0.2054
agec18 6 -0.07566 0.04415 891 -1.71 0.0869
Intercept 7 -0.04482 0.1678 904 -0.27 0.7895
agec18 7 -0.07062 0.04632 818 -1.52 0.1278
Intercept 8 0.2905 0.1639 961 1.77 0.0767
agec18 8 0.008061 0.04459 880 0.18 0.8566
Intercept 9 0.02859 0.1694 870 0.17 0.8660
agec18 9 -0.07439 0.04762 777 -1.56 0.1187
Intercept 10 0.2621 0.1621 961 1.62 0.1063
agec18 10 -0.04685 0.04555 865 -1.03 0.3040
Intercept 11 -0.5848 0.1715 869 -3.41 0.0007
agec18 11 0.09925 0.04665 791 2.13 0.0337
Intercept 12 0.3300 0.1688 909 1.95 0.0509
agec18 12 -0.02752 0.04551 833 -0.60 0.5456
Intercept 13 -0.1162 0.1658 943 -0.70 0.4835
agec18 13 -0.05157 0.04477 866 -1.15 0.2498
Intercept 14 0.02071 0.1634 950 0.13 0.8992
agec18 14 -0.00471 0.04563 857 -0.10 0.9178
Intercept 15 0.6932 0.1729 868 4.01 <.0001
agec18 15 0.01580 0.04607 798 0.34 0.7317
Intercept 16 -0.05395 0.1686 923 -0.32 0.7490
agec18 16 -0.09127 0.04467 855 -2.04 0.0414
Intercept 17 0.3304 0.1740 852 1.90 0.0579
agec18 17 -0.07233 0.04648 783 -1.56 0.1201
Intercept 18 -0.1948 0.1700 883 -1.15 0.2520
agec18 18 0.08386 0.04654 802 1.80 0.0719
Intercept 19 -0.06713 0.1692 905 -0.40 0.6916
agec18 19 0.09925 0.04560 829 2.18 0.0298
Intercept 20 -0.2064 0.1697 877 -1.22 0.2242
agec18 20 0.1016 0.04706 790 2.16 0.0311
Intercept 21 -0.7165 0.1649 950 -4.35 <.0001
agec18 21 0.02327 0.04482 869 0.52 0.6038
Intercept 22 0.08107 0.1694 882 0.48 0.6324
agec18 22 -0.04791 0.04690 795 -1.02 0.3073
Intercept 23 -0.5124 0.1712 861 -2.99 0.0028
agec18 23 0.1801 0.04732 776 3.81 0.0002
Intercept 24 -0.4209 0.1691 901 -2.49 0.0130
agec18 24 0.02210 0.04586 823 0.48 0.6301
Intercept 25 0.03568 0.1720 854 0.21 0.8357
agec18 25 0.02036 0.04734 772 0.43 0.6673
Intercept 26 0.1268 0.1679 920 0.76 0.4503
agec18 26 0.08701 0.04522 845 1.92 0.0547
Intercept 27 -0.9341 0.1681 902 -5.56 <.0001
agec18 27 0.004252 0.04632 817 0.09 0.9269
Intercept 28 0.4063 0.1616 990 2.51 0.0121
agec18 28 -0.1299 0.04383 910 -2.96 0.0031
Intercept 29 -1.0016 0.1678 916 -5.97 <.0001
agec18 29 0.06555 0.04552 838 1.44 0.1502
Intercept 30 0.5399 0.1704 901 3.17 0.0016
agec18 30 -0.1670 0.04521 832 -3.69 0.0002
Intercept 31 0.6022 0.1771 800 3.40 0.0007
agec18 31 0.08037 0.04817 726 1.67 0.0956
Intercept 32 -0.1710 0.1691 894 -1.01 0.3120
agec18 32 0.01370 0.04629 812 0.30 0.7673
Intercept 33 0.3661 0.1693 908 2.16 0.0308
agec18 33 -0.1583 0.04531 836 -3.49 0.0005
Intercept 34 0.2970 0.1715 893 1.73 0.0837
agec18 34 -0.05019 0.04515 828 -1.11 0.2666
Intercept 35 0.7823 0.1626 973 4.81 <.0001
agec18 35 -0.01138 0.04450 889 -0.26 0.7982
Intercept 36 -0.02760 0.1686 905 -0.16 0.8700
agec18 36 -0.1171 0.04586 825 -2.55 0.0108
Intercept 37 -0.1696 0.1708 866 -0.99 0.3212
agec18 37 -0.01542 0.04716 782 -0.33 0.7437
Intercept 38 -0.3138 0.1639 955 -1.92 0.0557
agec18 38 -0.01949 0.04504 869 -0.43 0.6654
Intercept 39 0.7081 0.1646 948 4.30 <.0001
agec18 39 0.01693 0.04507 865 0.38 0.7074
Intercept 40 -0.4700 0.1675 887 -2.81 0.0051
agec18 40 -0.06324 0.04752 789 -1.33 0.1837
Intercept 41 0.06818 0.1696 899 0.40 0.6878
agec18 41 0.2739 0.04569 824 6.00 <.0001
Intercept 42 -0.08596 0.1666 935 -0.52 0.6060
agec18 42 -0.01581 0.04492 858 -0.35 0.7250
Intercept 43 0.7132 0.1662 923 4.29 <.0001
agec18 43 0.02405 0.04591 836 0.52 0.6005
Intercept 44 -0.1864 0.1722 846 -1.08 0.2793
agec18 44 -0.03754 0.04770 762 -0.79 0.4316
Intercept 45 -0.06771 0.1665 932 -0.41 0.6844
agec18 45 0.09557 0.04515 853 2.12 0.0346
Intercept 46 0.2504 0.1652 966 1.52 0.1298
agec18 46 -0.02867 0.04358 898 -0.66 0.5107
Intercept 47 0.8147 0.1649 956 4.94 <.0001
agec18 47 0.1325 0.04435 881 2.99 0.0029
Intercept 48 0.07337 0.1684 927 0.44 0.6632
agec18 48 0.01157 0.04449 860 0.26 0.7948
Intercept 49 0.3728 0.1671 929 2.23 0.0259
agec18 49 0.07106 0.04507 852 1.58 0.1152
Intercept 50 -0.4224 0.1681 903 -2.51 0.0121
agec18 50 0.04632 0.04621 819 1.00 0.3164
Intercept 51 -0.00675 0.1663 950 -0.04 0.9677
agec18 51 0.1557 0.04406 881 3.53 0.0004
Intercept 52 -0.02890 0.1693 908 -0.17 0.8645
agec18 52 -0.01521 0.04534 835 -0.34 0.7373
Intercept 53 0.2287 0.1683 894 1.36 0.1745
agec18 53 -0.1545 0.04672 805 -3.31 0.0010
Intercept 54 0.2446 0.1638 947 1.49 0.1357
agec18 54 -0.05959 0.04560 855 -1.31 0.1916
Intercept 55 0.4440 0.1698 892 2.61 0.0091
agec18 55 0.03562 0.04607 814 0.77 0.4396
Intercept 56 -0.1841 0.1690 906 -1.09 0.2764
agec18 56 -0.1129 0.04559 830 -2.48 0.0135
Intercept 57 -0.2629 0.1670 920 -1.57 0.1157
agec18 57 -0.01741 0.04568 838 -0.38 0.7033
Intercept 58 -0.3596 0.1699 887 -2.12 0.0346
agec18 58 0.09352 0.04634 807 2.02 0.0439
Intercept 59 -0.4419 0.1675 927 -2.64 0.0085
agec18 59 -0.07092 0.04498 852 -1.58 0.1152
Intercept 60 0.08147 0.1665 933 0.49 0.6248
agec18 60 0.03673 0.04509 855 0.81 0.4156
Intercept 61 0.3548 0.1670 922 2.12 0.0339
agec18 61 -0.1011 0.04556 841 -2.22 0.0267
Intercept 62 -0.4218 0.1638 944 -2.58 0.0102
agec18 62 0.1152 0.04581 850 2.51 0.0121
Intercept 63 0.7605 0.1647 943 4.62 <.0001
agec18 63 0.08367 0.04538 857 1.84 0.0655
Intercept 64 -0.4936 0.1681 911 -2.94 0.0034
agec18 64 0.007629 0.04571 831 0.17 0.8675
Intercept 65 -0.1595 0.1719 837 -0.93 0.3539
agec18 65 0.1272 0.04837 746 2.63 0.0087
Intercept 66 0.1107 0.1704 896 0.65 0.5162
agec18 66 0.01597 0.04548 825 0.35 0.7256
Intercept 67 0.4562 0.1691 904 2.70 0.0071
agec18 67 -0.03950 0.04566 828 -0.87 0.3872
Intercept 68 -0.4734 0.1666 932 -2.84 0.0046
agec18 68 -0.02196 0.04511 854 -0.49 0.6265
Intercept 69 -0.3913 0.1641 946 -2.38 0.0173
agec18 69 0.1184 0.04554 856 2.60 0.0095
Intercept 70 0.09257 0.1673 928 0.55 0.5802
agec18 70 0.1025 0.04504 852 2.28 0.0231
Intercept 71 0.009042 0.1693 902 0.05 0.9574
agec18 71 -0.03461 0.04570 826 -0.76 0.4491
Intercept 72 -0.1295 0.1656 932 -0.78 0.4343
agec18 72 0.2657 0.04567 845 5.82 <.0001
Intercept 73 0.4334 0.1729 874 2.51 0.0124
agec18 73 -0.1204 0.04571 807 -2.63 0.0086
Intercept 74 -0.2338 0.1688 904 -1.39 0.1662
agec18 74 0.1107 0.04581 826 2.42 0.0159
Intercept 75 0.2943 0.1652 937 1.78 0.0752
agec18 75 0.06005 0.04551 851 1.32 0.1874
Intercept 76 -0.2631 0.1706 894 -1.54 0.1235
agec18 76 -0.03222 0.04555 823 -0.71 0.4796
Intercept 77 -0.07405 0.1746 835 -0.42 0.6716
agec18 77 -0.03616 0.04726 761 -0.77 0.4444
Intercept 78 -0.02689 0.1696 875 -0.16 0.8741
agec18 78 0.1679 0.04724 786 3.55 0.0004
Intercept 79 0.2115 0.1661 951 1.27 0.2031
agec18 79 0.01242 0.04409 881 0.28 0.7783
Intercept 80 -0.09415 0.1692 889 -0.56 0.5781
agec18 80 -0.1377 0.04655 805 -2.96 0.0032
Intercept 81 0.01021 0.1642 963 0.06 0.9504
agec18 81 0.05234 0.04424 887 1.18 0.2371
Intercept 82 -0.2366 0.1698 897 -1.39 0.1640
agec18 82 -0.08865 0.04572 822 -1.94 0.0529
Intercept 83 0.4675 0.1657 943 2.82 0.0049
agec18 83 0.05317 0.04489 864 1.18 0.2365
Intercept 84 -0.4149 0.1670 918 -2.48 0.0131
agec18 84 -0.1021 0.04586 833 -2.23 0.0263
Intercept 85 -0.1923 0.1622 988 -1.19 0.2359
agec18 85 -0.01170 0.04363 912 -0.27 0.7887
Intercept 86 -0.5997 0.1729 858 -3.47 0.0006
agec18 86 0.03300 0.04661 785 0.71 0.4791
Intercept 87 0.05781 0.1698 899 0.34 0.7336
agec18 87 0.09144 0.04559 826 2.01 0.0452
Intercept 88 -0.6273 0.1683 898 -3.73 0.0002
agec18 88 -0.06174 0.04643 813 -1.33 0.1840
Intercept 89 0.6059 0.1690 903 3.59 0.0004
agec18 89 0.2106 0.04578 825 4.60 <.0001
Intercept 90 -0.3286 0.1666 936 -1.97 0.0489
agec18 90 -0.1054 0.04485 860 -2.35 0.0190
Intercept 91 0.05954 0.1607 987 0.37 0.7111
agec18 91 -0.03353 0.04448 898 -0.75 0.4512
Intercept 92 0.2708 0.1690 879 1.60 0.1094
agec18 92 -0.1542 0.04730 788 -3.26 0.0012
Intercept 93 0.2656 0.1655 947 1.60 0.1089
agec18 93 -0.2098 0.04472 869 -4.69 <.0001
Intercept 94 -0.3347 0.1706 847 -1.96 0.0501
agec18 94 0.03164 0.04848 750 0.65 0.5141
Intercept 95 -0.1633 0.1636 960 -1.00 0.3184
agec18 95 -0.1126 0.04481 876 -2.51 0.0122
Intercept 96 -0.2539 0.1653 939 -1.54 0.1250
agec18 96 0.04006 0.04533 855 0.88 0.3771
Intercept 97 -0.2864 0.1684 910 -1.70 0.0893
agec18 97 -0.02019 0.04564 832 -0.44 0.6584
Intercept 98 -0.07525 0.1686 893 -0.45 0.6555
agec18 98 0.07519 0.04660 807 1.61 0.1070
Intercept 99 0.4229 0.1692 901 2.50 0.0126
agec18 99 0.08571 0.04579 824 1.87 0.0616
Intercept 100 0.7226 0.1703 890 4.24 <.0001
agec18 100 -0.07704 0.04596 814 -1.68 0.0941
Intercept 101 0.1671 0.1657 935 1.01 0.3136
agec18 101 -0.01162 0.04538 852 -0.26 0.7979
Intercept 102 0.003512 0.1736 825 0.02 0.9839
agec18 102 0.02257 0.04835 739 0.47 0.6408
Intercept 103 0.1075 0.1655 940 0.65 0.5162
agec18 103 -0.1940 0.04520 857 -4.29 <.0001
Intercept 104 1.0842 0.1628 982 6.66 <.0001
agec18 104 -0.08193 0.04371 907 -1.87 0.0612
Intercept 105 -0.00959 0.1689 882 -0.06 0.9547
agec18 105 0.009429 0.04715 792 0.20 0.8415
Intercept 106 -0.1749 0.1668 940 -1.05 0.2945
agec18 106 0.09829 0.04448 868 2.21 0.0274
Intercept 107 0.6552 0.1656 953 3.96 <.0001
agec18 107 -0.1067 0.04421 881 -2.41 0.0160
Intercept 108 0.2148 0.1655 908 1.30 0.1948
agec18 108 0.02744 0.04723 806 0.58 0.5614
Intercept 109 -0.08320 0.1672 932 -0.50 0.6188
agec18 109 0.08331 0.04486 857 1.86 0.0636
Intercept 110 0.4906 0.1690 915 2.90 0.0038
agec18 110 0.04642 0.04502 844 1.03 0.3028
Intercept 111 -0.1362 0.1658 952 -0.82 0.4117
agec18 111 0.1164 0.04415 881 2.64 0.0085
Intercept 112 -1.5626 0.1658 939 -9.42 <.0001
agec18 112 0.08941 0.04507 859 1.98 0.0476
Intercept 113 -0.00402 0.1657 938 -0.02 0.9806
agec18 113 0.1683 0.04523 855 3.72 0.0002
Intercept 114 0.2422 0.1691 863 1.43 0.1525
agec18 114 0.04348 0.04820 765 0.90 0.3673
Intercept 115 1.0131 0.1649 957 6.14 <.0001
agec18 115 0.05280 0.04432 882 1.19 0.2338
Intercept 116 0.1564 0.1705 876 0.92 0.3592
agec18 116 -0.1721 0.04670 795 -3.69 0.0002
Intercept 117 0.8262 0.1653 949 5.00 <.0001
agec18 117 -0.1425 0.04464 872 -3.19 0.0015
Intercept 118 0.1588 0.1640 956 0.97 0.3331
agec18 118 0.1202 0.04491 872 2.68 0.0076
Intercept 119 -0.4584 0.1643 953 -2.79 0.0054
agec18 119 0.2396 0.04492 870 5.33 <.0001
Intercept 120 -0.1549 0.1710 867 -0.91 0.3652
agec18 120 -0.06070 0.04702 784 -1.29 0.1972
Intercept 121 0.2870 0.1681 900 1.71 0.0881
agec18 121 -0.04335 0.04643 814 -0.93 0.3508
Intercept 122 -0.5604 0.1733 858 -3.23 0.0013
agec18 122 0.04879 0.04645 787 1.05 0.2939
Intercept 123 -0.3041 0.1711 852 -1.78 0.0758
agec18 123 0.03955 0.04792 762 0.83 0.4094
Intercept 124 -0.1065 0.1642 954 -0.65 0.5168
agec18 124 -0.02111 0.04492 870 -0.47 0.6385
Intercept 125 0.1392 0.1664 930 0.84 0.4032
agec18 125 -0.1132 0.04537 848 -2.49 0.0128
Intercept 126 -0.3143 0.1724 859 -1.82 0.0686
agec18 126 -0.01763 0.04685 782 -0.38 0.7068
Intercept 127 0.01715 0.1643 948 0.10 0.9169
agec18 127 -0.03118 0.04527 861 -0.69 0.4911
Intercept 128 0.05209 0.1710 883 0.30 0.7607
agec18 128 -0.04302 0.04605 809 -0.93 0.3505
Intercept 129 -0.4898 0.1757 831 -2.79 0.0054
agec18 129 -0.07694 0.04700 762 -1.64 0.1021
Intercept 130 0.1139 0.1693 897 0.67 0.5010
agec18 130 -0.1314 0.04604 818 -2.85 0.0044
Intercept 131 -0.2507 0.1682 894 -1.49 0.1365
agec18 131 -0.03944 0.04671 806 -0.84 0.3987
Intercept 132 -0.9910 0.1651 929 -6.00 <.0001
agec18 132 -0.04195 0.04615 836 -0.91 0.3636
Intercept 133 0.5354 0.1643 974 3.26 0.0012
agec18 133 -0.1226 0.04347 905 -2.82 0.0049
Intercept 134 0.3162 0.1615 986 1.96 0.0505
agec18 134 -0.03379 0.04413 903 -0.77 0.4440
Intercept 135 -0.5265 0.1708 884 -3.08 0.0021
agec18 135 -0.08147 0.04607 809 -1.77 0.0774
Intercept 136 -0.02906 0.1746 835 -0.17 0.8679
agec18 136 -0.00943 0.04724 761 -0.20 0.8419
Intercept 137 -0.1631 0.1652 927 -0.99 0.3235
agec18 137 -0.08807 0.04622 834 -1.91 0.0571
Intercept 138 -0.4292 0.1680 905 -2.56 0.0108
agec18 138 -0.1813 0.04618 820 -3.93 <.0001
Intercept 139 0.2239 0.1715 871 1.31 0.1920
agec18 139 -0.05003 0.04655 794 -1.07 0.2828
Intercept 140 0.09886 0.1679 891 0.59 0.5562
agec18 140 -0.1358 0.04708 798 -2.88 0.0040
Intercept 141 0.6894 0.1656 931 4.16 <.0001
agec18 141 0.05745 0.04568 845 1.26 0.2088
Intercept 142 0.1519 0.1662 933 0.91 0.3611
agec18 142 0.05770 0.04527 852 1.27 0.2028
Intercept 143 -0.1518 0.1703 881 -0.89 0.3732
agec18 143 -0.05507 0.04647 801 -1.19 0.2363
Intercept 144 -0.4350 0.1663 938 -2.62 0.0091
agec18 144 0.09769 0.04488 861 2.18 0.0298
Intercept 145 -0.2807 0.1685 916 -1.67 0.0960
agec18 145 -0.04364 0.04521 842 -0.97 0.3347
Intercept 146 -0.1638 0.1690 914 -0.97 0.3327
agec18 146 -0.2000 0.04509 843 -4.44 <.0001
Intercept 147 -0.9005 0.1684 889 -5.35 <.0001
agec18 147 0.05253 0.04695 799 1.12 0.2636
Intercept 148 -0.6556 0.1644 948 -3.99 <.0001
agec18 148 0.07146 0.04521 862 1.58 0.1143
Intercept 149 -0.06122 0.1738 839 -0.35 0.7248
agec18 149 -0.1803 0.04737 762 -3.81 0.0002
Intercept 150 0.3694 0.1652 952 2.24 0.0256
agec18 150 -0.00035 0.04448 876 -0.01 0.9937
Intercept 151 0.1802 0.1680 917 1.07 0.2836
agec18 151 0.05453 0.04537 841 1.20 0.2297
Intercept 152 0.5254 0.1657 934 3.17 0.0016
agec18 152 0.05998 0.04547 849 1.32 0.1875
Intercept 153 -0.8246 0.1658 939 -4.97 <.0001
agec18 153 0.06770 0.04505 859 1.50 0.1333
Intercept 154 0.4636 0.1645 947 2.82 0.0049
agec18 154 -0.05311 0.04524 861 -1.17 0.2407
Intercept 155 0.2381 0.1659 917 1.44 0.1516
agec18 155 0.01207 0.04647 824 0.26 0.7951
Intercept 156 0.3797 0.1757 833 2.16 0.0310
agec18 156 0.09571 0.04682 766 2.04 0.0413
Intercept 157 -0.1757 0.1628 948 -1.08 0.2808
agec18 157 0.1100 0.04610 848 2.39 0.0173
Intercept 158 -0.1924 0.1678 907 -1.15 0.2519
agec18 158 0.009048 0.04615 822 0.20 0.8446
Intercept 159 0.1018 0.1626 975 0.63 0.5315
agec18 159 -0.06919 0.04430 893 -1.56 0.1187
Intercept 160 -0.2704 0.1648 935 -1.64 0.1012
agec18 160 0.01049 0.04587 844 0.23 0.8191
Intercept 161 -0.1160 0.1663 936 -0.70 0.4857
agec18 161 0.000690 0.04499 858 0.02 0.9878
Intercept 162 -0.2963 0.1720 886 -1.72 0.0853
agec18 162 0.05097 0.04539 820 1.12 0.2618
Intercept 163 -0.2889 0.1621 963 -1.78 0.0749
agec18 163 0.1210 0.04548 867 2.66 0.0079
Intercept 164 -0.1122 0.1691 894 -0.66 0.5072
agec18 164 -0.07120 0.04630 812 -1.54 0.1245
Intercept 165 -0.8622 0.1698 889 -5.08 <.0001
agec18 165 0.1230 0.04630 808 2.66 0.0080
Intercept 166 0.5151 0.1648 940 3.13 0.0018
agec18 166 -0.02481 0.04550 853 -0.55 0.5858
Intercept 167 0.1026 0.1653 936 0.62 0.5350
agec18 167 -0.05660 0.04553 850 -1.24 0.2141
Intercept 168 -0.2307 0.1692 884 -1.36 0.1732
agec18 168 0.06792 0.04683 798 1.45 0.1473
Intercept 169 -0.6009 0.1721 865 -3.49 0.0005
agec18 169 -0.1386 0.04660 789 -2.97 0.0030
Intercept 170 -0.1171 0.1716 852 -0.68 0.4951
agec18 170 -0.07036 0.04762 767 -1.48 0.1399
Intercept 171 0.1369 0.1657 938 0.83 0.4088
agec18 171 0.09026 0.04520 856 2.00 0.0462
Intercept 172 1.0455 0.1706 884 6.13 <.0001
agec18 172 -0.09511 0.04616 808 -2.06 0.0397
Intercept 173 -0.1484 0.1636 968 -0.91 0.3645
agec18 173 0.01392 0.04425 890 0.31 0.7532
Intercept 174 0.1417 0.1651 950 0.86 0.3909
agec18 174 0.03772 0.04469 871 0.84 0.3989
Intercept 175 -0.5084 0.1643 955 -3.09 0.0020
agec18 175 0.1120 0.04481 873 2.50 0.0126
Intercept 176 -0.4903 0.1722 865 -2.85 0.0045
agec18 176 -0.05760 0.04654 790 -1.24 0.2162
Intercept 177 0.2857 0.1659 935 1.72 0.0853
agec18 177 -0.01797 0.04528 853 -0.40 0.6916
Intercept 178 0.1412 0.1647 959 0.86 0.3915
agec18 178 -0.01863 0.04429 883 -0.42 0.6741
Intercept 179 0.2184 0.1650 939 1.32 0.1862
agec18 179 0.04559 0.04545 853 1.00 0.3162
Intercept 180 0.3078 0.1701 878 1.81 0.0707
agec18 180 -0.00190 0.04678 795 -0.04 0.9676
Intercept 181 -0.1017 0.1686 898 -0.60 0.5466
agec18 181 0.2116 0.04631 814 4.57 <.0001
Intercept 182 -0.4705 0.1667 925 -2.82 0.0049
agec18 182 0.04957 0.04559 842 1.09 0.2772
Intercept 183 -0.2226 0.1648 953 -1.35 0.1771
agec18 183 -0.04687 0.04461 875 -1.05 0.2938
Intercept 184 -0.2443 0.1730 850 -1.41 0.1584
agec18 184 0.05191 0.04707 773 1.10 0.2704
Intercept 185 -0.2005 0.1672 913 -1.20 0.2308
agec18 185 -0.03286 0.04608 827 -0.71 0.4759
Intercept 186 -0.3781 0.1689 896 -2.24 0.0254
agec18 186 0.1060 0.04631 813 2.29 0.0224
Intercept 187 -0.3067 0.1702 863 -1.80 0.0719
agec18 187 0.02953 0.04770 772 0.62 0.5360
Intercept 188 0.1937 0.1680 919 1.15 0.2492
agec18 188 0.1425 0.04524 844 3.15 0.0017
Intercept 189 -0.04058 0.1696 900 -0.24 0.8109
agec18 189 0.09318 0.04571 824 2.04 0.0418
Intercept 190 -0.1959 0.1687 887 -1.16 0.2459
agec18 190 0.000634 0.04693 798 0.01 0.9892
Intercept 191 0.007676 0.1713 883 0.04 0.9643
agec18 191 0.03613 0.04591 811 0.79 0.4315
Intercept 192 0.7844 0.1715 880 4.57 <.0001
agec18 192 0.06634 0.04601 807 1.44 0.1498
Intercept 193 -0.4298 0.1647 957 -2.61 0.0092
agec18 193 0.001635 0.04442 880 0.04 0.9706
Intercept 194 0.2454 0.1726 850 1.42 0.1555
agec18 194 -0.05719 0.04729 770 -1.21 0.2269
Intercept 195 -0.04457 0.1671 923 -0.27 0.7898
agec18 195 -0.02936 0.04545 843 -0.65 0.5184
Intercept 196 0.07460 0.1678 916 0.44 0.6567
agec18 196 -0.1855 0.04554 837 -4.07 <.0001
Intercept 197 -0.07436 0.1694 893 -0.44 0.6608
agec18 197 0.02062 0.04623 812 0.45 0.6557
Intercept 198 0.4056 0.1690 891 2.40 0.0166
agec18 198 -0.1230 0.04656 806 -2.64 0.0084
Intercept 199 0.5119 0.1685 896 3.04 0.0025
agec18 199 -0.03069 0.04645 811 -0.66 0.5090
Intercept 200 0.1422 0.1657 906 0.86 0.3909
agec18 200 0.01794 0.04729 804 0.38 0.7046

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 200 0.16 0.16 0.6861 0.6865


Descriptives for Random Effects and Residuals

The MEANS Procedure

Variable N Mean Variance Minimum Maximum
monUint
monUage
monEres
1400
1400
1400
0.0650000
-1.62398E-15
9.920636E-16
0.1682045
0.0084447
0.0616256
-1.4975944
-0.2221221
-0.7779043
1.1492182
0.2739340
0.7572650


Ch 9 Eq. 9.6: Predicting Risky Behavior from Monitoring Random Effects and Residuals

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 10
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8065.96987467  
1 2 7430.80148889 0.00000000

Convergence criteria met.

Estimated G Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2
1 Intercept 1 15.5934 1.7982
2 agec18 1 1.7982 0.4981

Estimated G Correlation Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2
1 Intercept 1 1.0000 0.6452
2 agec18 1 0.6452 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 15.5934 1.9076 8.17 <.0001
UN(2,1) PersonID 1.7982 0.3275 5.49 <.0001
UN(2,2) PersonID 0.4981 0.07694 6.47 <.0001
Residual   7.3316 0.3279 22.36 <.0001

Fit Statistics
-2 Log Likelihood 7430.8
AIC (Smaller is Better) 7458.8
AICC (Smaller is Better) 7459.1
BIC (Smaller is Better) 7505.0

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 635.17 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7430.8 14 7458.8 7459.1 7477.5 7505.0 7519.0

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.5971 0.3295 244 71.62 <.0001 0.05 22.9481 24.2460
agec18 2.0098 0.1384 1186 14.52 <.0001 0.05 1.7382 2.2813
agec18*agec18 0.1465 0.02058 1010 7.12 <.0001 0.05 0.1062 0.1869
att4 -3.3318 0.5137 200 -6.49 <.0001 0.05 -4.3447 -2.3188
agec18*att4 -0.5294 0.1027 199 -5.15 <.0001 0.05 -0.7319 -0.3268
monUint -4.3591 0.7652 202 -5.70 <.0001 0.05 -5.8679 -2.8502
agec18*monUint -0.5475 0.1530 201 -3.58 0.0004 0.05 -0.8492 -0.2458
monUage 3.7561 3.4127 202 1.10 0.2724 0.05 -2.9730 10.4851
agec18*monUage -1.7481 0.6858 206 -2.55 0.0115 0.05 -3.1001 -0.3960
monEres 3.5580 0.3013 1000 11.81 <.0001 0.05 2.9667 4.1493

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1186 210.82 210.82 <.0001 <.0001
agec18*agec18 1 1010 50.70 50.70 <.0001 <.0001
att4 1 200 42.07 42.07 <.0001 <.0001
agec18*att4 1 199 26.56 26.56 <.0001 <.0001
monUint 1 202 32.45 32.45 <.0001 <.0001
agec18*monUint 1 201 12.80 12.80 0.0003 0.0004
monUage 1 202 1.21 1.21 0.2711 0.2724
agec18*monUage 1 206 6.50 6.50 0.0108 0.0115
monEres 1 1000 139.42 139.42 <.0001 <.0001


Total R2 (% Reduction) for PredAttOnly vs. PredMonRandom

Name PredCorr TotalR2 TotalR2Diff
PredAttOnly 0.48557 0.23578 .
PredMonRandom 0.58091 0.33746 0.10168


Ch 9 Eq. 9.6: Predicting Risky Behavior from Monitoring Random Effects and Residuals
Adding Random Effect of WP Monitoring Residual

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 7
Columns in X 10
Columns in Z per Subject 3
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8065.96987467  
1 4 7420.55130172 .
2 1 7420.49347192 0.00000010
3 1 7420.49322539 0.00000000

Convergence criteria met.

Estimated G Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2 Col3
1 Intercept 1 15.5780 1.8008 3.1339
2 agec18 1 1.8008 0.5036 0.01326
3 monEres 1 3.1339 0.01326 0.2623

Estimated G Correlation Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2 Col3
1 Intercept 1 1.0000 0.6429 1.0000
2 agec18 1 0.6429 1.0000 0.03649
3 monEres 1 1.0000 0.03649 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 15.5780 1.9051 8.18 <.0001
UN(2,1) PersonID 1.8008 0.3279 5.49 <.0001
UN(2,2) PersonID 0.5036 0.07737 6.51 <.0001
UN(3,1) PersonID 3.1339 1.3211 2.37 0.0177
UN(3,2) PersonID 0.01326 0.2708 0.05 0.9609
UN(3,3) PersonID 0.2623 1.5358 0.17 0.4322
Residual   7.3009 0.3468 21.05 <.0001

Fit Statistics
-2 Log Likelihood 7420.5
AIC (Smaller is Better) 7454.5
AICC (Smaller is Better) 7454.9
BIC (Smaller is Better) 7510.6

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
6 645.48 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7420.5 17 7454.5 7454.9 7477.2 7510.6 7527.6

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.6060 0.3281 244 71.94 <.0001 0.05 22.9597 24.2523
agec18 2.0108 0.1379 1185 14.58 <.0001 0.05 1.7403 2.2813
agec18*agec18 0.1466 0.02046 1009 7.17 <.0001 0.05 0.1065 0.1868
att4 -3.2060 0.5053 199 -6.34 <.0001 0.05 -4.2024 -2.2096
agec18*att4 -0.5276 0.1030 199 -5.12 <.0001 0.05 -0.7307 -0.3246
monUint -4.3954 0.7537 202 -5.83 <.0001 0.05 -5.8816 -2.9092
agec18*monUint -0.5525 0.1534 201 -3.60 0.0004 0.05 -0.8549 -0.2502
monUage 3.6073 3.3739 205 1.07 0.2862 0.05 -3.0447 10.2592
agec18*monUage -1.7557 0.6872 205 -2.55 0.0113 0.05 -3.1105 -0.4008
monEres 3.5260 0.3005 161 11.73 <.0001 0.05 2.9325 4.1195

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1185 212.72 212.72 <.0001 <.0001
agec18*agec18 1 1009 51.36 51.36 <.0001 <.0001
att4 1 199 40.26 40.26 <.0001 <.0001
agec18*att4 1 199 26.26 26.26 <.0001 <.0001
monUint 1 202 34.01 34.01 <.0001 <.0001
agec18*monUint 1 201 12.98 12.98 0.0003 0.0004
monUage 1 205 1.14 1.14 0.2850 0.2862
agec18*monUage 1 205 6.53 6.53 0.0106 0.0113
monEres 1 161 137.65 137.65 <.0001 <.0001


Likelihood Ratio Test for FitUWPasfixed vs. FitUWPasrandom

Name Neg2LogLike Parms AIC BIC DevDiff DFdiff Pvalue
FitUWPasfixed 7430.8 14 7458.8 7505.0 . . .
FitUWPasrandom 7420.5 17 7454.5 7510.6 10.3083 3 0.016120


Ch 9 Eq. 9.6: Predicting Risky Behavior from Monitoring Random Effects and Residuals
Adding WP Monitoring by Age

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 11
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8062.13345090  
1 2 7421.15888678 0.00000000

Convergence criteria met.

Estimated G Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2
1 Intercept 1 15.6184 1.8042
2 agec18 1 1.8042 0.5004

Estimated G Correlation Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2
1 Intercept 1 1.0000 0.6453
2 agec18 1 0.6453 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 15.6184 1.9066 8.19 <.0001
UN(2,1) PersonID 1.8042 0.3273 5.51 <.0001
UN(2,2) PersonID 0.5004 0.07690 6.51 <.0001
Residual   7.2614 0.3248 22.36 <.0001

Fit Statistics
-2 Log Likelihood 7421.2
AIC (Smaller is Better) 7451.2
AICC (Smaller is Better) 7451.5
BIC (Smaller is Better) 7500.6

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 640.97 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7421.2 15 7451.2 7451.5 7471.2 7500.6 7515.6

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.6105 0.3293 244 71.70 <.0001 0.05 22.9619 24.2591
agec18 2.0063 0.1379 1185 14.55 <.0001 0.05 1.7358 2.2769
agec18*agec18 0.1452 0.02049 1009 7.09 <.0001 0.05 0.1050 0.1854
att4 -3.3061 0.5137 200 -6.44 <.0001 0.05 -4.3190 -2.2932
agec18*att4 -0.5208 0.1027 200 -5.07 <.0001 0.05 -0.7234 -0.3182
monUint -4.6150 0.7695 206 -6.00 <.0001 0.05 -6.1320 -3.0980
agec18*monUint -0.6001 0.1539 206 -3.90 0.0001 0.05 -0.9035 -0.2966
monUage 3.0606 3.4192 204 0.90 0.3718 0.05 -3.6810 9.8022
agec18*monUage -1.7786 0.6857 206 -2.59 0.0102 0.05 -3.1305 -0.4267
monEres 5.2010 0.6071 1095 8.57 <.0001 0.05 4.0098 6.3922
agec18*monEres 0.5445 0.1749 1122 3.11 0.0019 0.05 0.2013 0.8877

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1185 211.70 211.70 <.0001 <.0001
agec18*agec18 1 1009 50.24 50.24 <.0001 <.0001
att4 1 200 41.42 41.42 <.0001 <.0001
agec18*att4 1 200 25.69 25.69 <.0001 <.0001
monUint 1 206 35.97 35.97 <.0001 <.0001
agec18*monUint 1 206 15.20 15.20 <.0001 0.0001
monUage 1 204 0.80 0.80 0.3707 0.3718
agec18*monUage 1 206 6.73 6.73 0.0095 0.0102
monEres 1 1095 73.40 73.40 <.0001 <.0001
agec18*monEres 1 1122 9.69 9.69 0.0019 0.0019


Ch 9: Saving Predicted Fixed Effects and Residuals for Monitoring as Data

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable monitor
Covariance Structure Diagonal
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Residual

Dimensions
Covariance Parameters 1
Columns in X 400
Columns in Z 0
Subjects 1
Max Obs per Subject 1400

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Covariance Parameter Estimates
Cov Parm Estimate Standard Error Z Value Pr > Z
Residual 0.05767 0.002180 26.46 <.0001

Fit Statistics
-2 Log Likelihood -21.2
AIC (Smaller is Better) 780.8
AICC (Smaller is Better) 1103.9
BIC (Smaller is Better) 2883.8

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
-21.2 401 780.8 1103.9 1566.9 2883.8 3284.8

Solution for Fixed Effects
Effect PersonID: Person
ID Number		 Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
PersonID 1 2.7855 0.1621 1000 17.19 <.0001 0.05 2.4675 3.1035
PersonID 2 3.5854 0.1683 1000 21.31 <.0001 0.05 3.2552 3.9156
PersonID 3 3.4034 0.1694 1000 20.10 <.0001 0.05 3.0711 3.7358
PersonID 4 3.3524 0.1720 1000 19.49 <.0001 0.05 3.0150 3.6899
PersonID 5 3.6915 0.1672 1000 22.08 <.0001 0.05 3.3634 4.0195
PersonID 6 3.2534 0.1572 1000 20.69 <.0001 0.05 2.9448 3.5619
PersonID 7 2.9517 0.1639 1000 18.01 <.0001 0.05 2.6302 3.2733
PersonID 8 3.4139 0.1577 1000 21.65 <.0001 0.05 3.1044 3.7233
PersonID 9 3.0321 0.1670 1000 18.16 <.0001 0.05 2.7044 3.3598
PersonID 10 3.3374 0.1559 1000 21.40 <.0001 0.05 3.0314 3.6435
PersonID 11 2.4485 0.1690 1000 14.49 <.0001 0.05 2.1168 2.7801
PersonID 12 3.4367 0.1646 1000 20.88 <.0001 0.05 3.1137 3.7597
PersonID 13 2.8869 0.1602 1000 18.02 <.0001 0.05 2.5725 3.2013
PersonID 14 3.0858 0.1577 1000 19.57 <.0001 0.05 2.7764 3.3951
PersonID 15 3.9180 0.1704 1000 22.99 <.0001 0.05 3.5836 4.2524
PersonID 16 2.9269 0.1637 1000 17.88 <.0001 0.05 2.6057 3.2482
PersonID 17 3.3998 0.1723 1000 19.73 <.0001 0.05 3.0617 3.7380
PersonID 18 2.9045 0.1669 1000 17.40 <.0001 0.05 2.5770 3.2320
PersonID 19 3.0680 0.1651 1000 18.58 <.0001 0.05 2.7440 3.3921
PersonID 20 2.9075 0.1669 1000 17.42 <.0001 0.05 2.5800 3.2349
PersonID 21 2.2355 0.1590 1000 14.06 <.0001 0.05 1.9235 2.5476
PersonID 22 3.1201 0.1664 1000 18.75 <.0001 0.05 2.7935 3.4467
PersonID 23 2.6114 0.1691 1000 15.44 <.0001 0.05 2.2796 2.9432
PersonID 24 2.5797 0.1652 1000 15.62 <.0001 0.05 2.2556 2.9039
PersonID 25 3.1270 0.1703 1000 18.36 <.0001 0.05 2.7929 3.4611
PersonID 26 3.2872 0.1632 1000 20.14 <.0001 0.05 2.9670 3.6074
PersonID 27 1.9523 0.1642 1000 11.89 <.0001 0.05 1.6301 2.2746
PersonID 28 3.4484 0.1542 1000 22.37 <.0001 0.05 3.1458 3.7509
PersonID 29 1.9243 0.1633 1000 11.78 <.0001 0.05 1.6038 2.2447
PersonID 30 3.5725 0.1665 1000 21.45 <.0001 0.05 3.2457 3.8993
PersonID 31 3.8866 0.1780 1000 21.84 <.0001 0.05 3.5374 4.2359
PersonID 32 2.8719 0.1655 1000 17.35 <.0001 0.05 2.5470 3.1967
PersonID 33 3.3715 0.1650 1000 20.43 <.0001 0.05 3.0476 3.6954
PersonID 34 3.3798 0.1679 1000 20.13 <.0001 0.05 3.0502 3.7093
PersonID 35 3.9763 0.1558 1000 25.51 <.0001 0.05 3.6705 4.2821
PersonID 36 2.9334 0.1646 1000 17.82 <.0001 0.05 2.6103 3.2564
PersonID 37 2.8467 0.1685 1000 16.89 <.0001 0.05 2.5160 3.1774
PersonID 38 2.6797 0.1578 1000 16.98 <.0001 0.05 2.3699 2.9894
PersonID 39 3.9155 0.1589 1000 24.65 <.0001 0.05 3.6037 4.2272
PersonID 40 2.4475 0.1644 1000 14.89 <.0001 0.05 2.1250 2.7701
PersonID 41 3.3790 0.1658 1000 20.38 <.0001 0.05 3.0536 3.7044
PersonID 42 2.9503 0.1613 1000 18.29 <.0001 0.05 2.6338 3.2669
PersonID 43 3.9324 0.1615 1000 24.36 <.0001 0.05 3.6155 4.2492
PersonID 44 2.8040 0.1708 1000 16.41 <.0001 0.05 2.4688 3.1392
PersonID 45 3.0610 0.1614 1000 18.97 <.0001 0.05 2.7443 3.3776
PersonID 46 3.3397 0.1586 1000 21.06 <.0001 0.05 3.0284 3.6509
PersonID 47 4.1291 0.1588 1000 26.00 <.0001 0.05 3.8175 4.4408
PersonID 48 3.1618 0.1634 1000 19.35 <.0001 0.05 2.8412 3.4824
PersonID 49 3.5657 0.1621 1000 22.00 <.0001 0.05 3.2476 3.8838
PersonID 50 2.5995 0.1642 1000 15.83 <.0001 0.05 2.2773 2.9216
PersonID 51 3.1765 0.1604 1000 19.80 <.0001 0.05 2.8617 3.4913
PersonID 52 3.0177 0.1651 1000 18.28 <.0001 0.05 2.6937 3.3417
PersonID 53 3.2052 0.1648 1000 19.45 <.0001 0.05 2.8818 3.5287
PersonID 54 3.3073 0.1582 1000 20.91 <.0001 0.05 2.9969 3.6177
PersonID 55 3.6285 0.1664 1000 21.81 <.0001 0.05 3.3020 3.9550
PersonID 56 2.7500 0.1650 1000 16.67 <.0001 0.05 2.4263 3.0738
PersonID 57 2.7377 0.1624 1000 16.86 <.0001 0.05 2.4191 3.0563
PersonID 58 2.7145 0.1666 1000 16.29 <.0001 0.05 2.3876 3.0415
PersonID 59 2.4816 0.1626 1000 15.26 <.0001 0.05 2.1626 2.8006
PersonID 60 3.1911 0.1613 1000 19.78 <.0001 0.05 2.8746 3.5077
PersonID 61 3.4049 0.1623 1000 20.98 <.0001 0.05 3.0864 3.7234
PersonID 62 2.6577 0.1583 1000 16.79 <.0001 0.05 2.3472 2.9682
PersonID 63 4.0307 0.1592 1000 25.32 <.0001 0.05 3.7183 4.3430
PersonID 64 2.4820 0.1639 1000 15.15 <.0001 0.05 2.1604 2.8035
PersonID 65 2.9936 0.1710 1000 17.50 <.0001 0.05 2.6580 3.3292
PersonID 66 3.2115 0.1667 1000 19.26 <.0001 0.05 2.8843 3.5388
PersonID 67 3.5779 0.1651 1000 21.67 <.0001 0.05 3.2539 3.9019
PersonID 68 2.4849 0.1614 1000 15.40 <.0001 0.05 2.1681 2.8016
PersonID 69 2.6956 0.1584 1000 17.01 <.0001 0.05 2.3847 3.0065
PersonID 70 3.2577 0.1623 1000 20.07 <.0001 0.05 2.9392 3.5762
PersonID 71 3.0466 0.1654 1000 18.42 <.0001 0.05 2.7222 3.3711
PersonID 72 3.1249 0.1605 1000 19.47 <.0001 0.05 2.8100 3.4398
PersonID 73 3.4828 0.1702 1000 20.47 <.0001 0.05 3.1489 3.8167
PersonID 74 2.8785 0.1648 1000 17.47 <.0001 0.05 2.5551 3.2019
PersonID 75 3.4614 0.1599 1000 21.65 <.0001 0.05 3.1477 3.7752
PersonID 76 2.7213 0.1670 1000 16.29 <.0001 0.05 2.3936 3.0491
PersonID 77 2.9403 0.1737 1000 16.92 <.0001 0.05 2.5994 3.2813
PersonID 78 3.1829 0.1670 1000 19.06 <.0001 0.05 2.8552 3.5106
PersonID 79 3.3253 0.1601 1000 20.77 <.0001 0.05 3.0111 3.6395
PersonID 80 2.8325 0.1659 1000 17.07 <.0001 0.05 2.5069 3.1581
PersonID 81 3.1164 0.1578 1000 19.75 <.0001 0.05 2.8067 3.4261
PersonID 82 2.7058 0.1661 1000 16.29 <.0001 0.05 2.3798 3.0318
PersonID 83 3.6609 0.1601 1000 22.86 <.0001 0.05 3.3467 3.9750
PersonID 84 2.4869 0.1624 1000 15.31 <.0001 0.05 2.1682 2.8057
PersonID 85 2.8310 0.1548 1000 18.28 <.0001 0.05 2.5271 3.1348
PersonID 86 2.3687 0.1709 1000 13.86 <.0001 0.05 2.0333 2.7042
PersonID 87 3.2118 0.1660 1000 19.35 <.0001 0.05 2.8860 3.5375
PersonID 88 2.2620 0.1646 1000 13.75 <.0001 0.05 1.9391 2.5850
PersonID 89 3.9677 0.1650 1000 24.05 <.0001 0.05 3.6439 4.2915
PersonID 90 2.5912 0.1613 1000 16.07 <.0001 0.05 2.2747 2.9077
PersonID 91 3.1105 0.1535 1000 20.26 <.0001 0.05 2.8093 3.4117
PersonID 92 3.2528 0.1662 1000 19.58 <.0001 0.05 2.9267 3.5789
PersonID 93 3.2166 0.1598 1000 20.14 <.0001 0.05 2.9031 3.5301
PersonID 94 2.6909 0.1692 1000 15.90 <.0001 0.05 2.3588 3.0230
PersonID 95 2.7869 0.1574 1000 17.71 <.0001 0.05 2.4781 3.0957
PersonID 96 2.7959 0.1599 1000 17.48 <.0001 0.05 2.4820 3.1097
PersonID 97 2.7060 0.1641 1000 16.49 <.0001 0.05 2.3839 3.0281
PersonID 98 3.0398 0.1651 1000 18.41 <.0001 0.05 2.7158 3.3639
PersonID 99 3.6441 0.1653 1000 22.04 <.0001 0.05 3.3196 3.9685
PersonID 100 3.8668 0.1669 1000 23.16 <.0001 0.05 3.5392 4.1943
PersonID 101 3.2539 0.1604 1000 20.28 <.0001 0.05 2.9391 3.5688
PersonID 102 3.0913 0.1732 1000 17.84 <.0001 0.05 2.7514 3.4313
PersonID 103 3.0392 0.1600 1000 18.99 <.0001 0.05 2.7252 3.3533
PersonID 104 4.2799 0.1557 1000 27.49 <.0001 0.05 3.9744 4.5854
PersonID 105 3.0618 0.1659 1000 18.46 <.0001 0.05 2.7363 3.3874
PersonID 106 2.9342 0.1613 1000 18.19 <.0001 0.05 2.6177 3.2507
PersonID 107 3.7593 0.1596 1000 23.55 <.0001 0.05 3.4461 4.0725
PersonID 108 3.3436 0.1615 1000 20.70 <.0001 0.05 3.0267 3.6605
PersonID 109 3.0325 0.1620 1000 18.72 <.0001 0.05 2.7146 3.3504
PersonID 110 3.6897 0.1644 1000 22.44 <.0001 0.05 3.3670 4.0124
PersonID 111 2.9928 0.1598 1000 18.72 <.0001 0.05 2.6791 3.3064
PersonID 112 1.2823 0.1604 1000 7.99 <.0001 0.05 0.9676 1.5971
PersonID 113 3.1937 0.1603 1000 19.92 <.0001 0.05 2.8791 3.5082
PersonID 114 3.3960 0.1670 1000 20.33 <.0001 0.05 3.0682 3.7238
PersonID 115 4.3027 0.1587 1000 27.11 <.0001 0.05 3.9912 4.6141
PersonID 116 3.1004 0.1677 1000 18.49 <.0001 0.05 2.7713 3.4296
PersonID 117 3.9330 0.1595 1000 24.66 <.0001 0.05 3.6201 4.2460
PersonID 118 3.3446 0.1580 1000 21.17 <.0001 0.05 3.0346 3.6546
PersonID 119 2.7080 0.1584 1000 17.10 <.0001 0.05 2.3972 3.0188
PersonID 120 2.8234 0.1687 1000 16.74 <.0001 0.05 2.4924 3.1543
PersonID 121 3.3710 0.1643 1000 20.52 <.0001 0.05 3.0487 3.6934
PersonID 122 2.4302 0.1713 1000 14.19 <.0001 0.05 2.0940 2.7664
PersonID 123 2.7346 0.1694 1000 16.14 <.0001 0.05 2.4021 3.0671
PersonID 124 2.9230 0.1582 1000 18.48 <.0001 0.05 2.6126 3.2334
PersonID 125 3.1394 0.1614 1000 19.45 <.0001 0.05 2.8226 3.4561
PersonID 126 2.6687 0.1704 1000 15.67 <.0001 0.05 2.3344 3.0030
PersonID 127 3.0610 0.1585 1000 19.31 <.0001 0.05 2.7499 3.3721
PersonID 128 3.0902 0.1679 1000 18.41 <.0001 0.05 2.7608 3.4196
PersonID 129 2.3933 0.1749 1000 13.68 <.0001 0.05 2.0500 2.7366
PersonID 130 3.0893 0.1656 1000 18.66 <.0001 0.05 2.7644 3.4142
PersonID 131 2.7311 0.1647 1000 16.58 <.0001 0.05 2.4078 3.0543
PersonID 132 1.8560 0.1602 1000 11.59 <.0001 0.05 1.5417 2.1703
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agec18*PersonID 158 -0.00043 0.04585 1000 -0.01 0.9925 0.05 -0.09040 0.08954
agec18*PersonID 159 -0.08577 0.04318 1000 -1.99 0.0473 0.05 -0.1705 -0.00104
agec18*PersonID 160 -0.00160 0.04530 1000 -0.04 0.9718 0.05 -0.09050 0.08730
agec18*PersonID 161 -0.00739 0.04425 1000 -0.17 0.8674 0.05 -0.09424 0.07945
agec18*PersonID 162 0.04779 0.04510 1000 1.06 0.2896 0.05 -0.04072 0.1363
agec18*PersonID 163 0.1382 0.04465 1000 3.10 0.0020 0.05 0.05060 0.2258
agec18*PersonID 164 -0.1000 0.04612 1000 -2.17 0.0303 0.05 -0.1906 -0.00954
agec18*PersonID 165 0.1146 0.04617 1000 2.48 0.0132 0.05 0.02404 0.2052
agec18*PersonID 166 -0.01282 0.04483 1000 -0.29 0.7749 0.05 -0.1008 0.07515
agec18*PersonID 167 -0.07081 0.04489 1000 -1.58 0.1150 0.05 -0.1589 0.01728
agec18*PersonID 168 0.07369 0.04683 1000 1.57 0.1159 0.05 -0.01820 0.1656
agec18*PersonID 169 -0.2118 0.04671 1000 -4.53 <.0001 0.05 -0.3034 -0.1201
agec18*PersonID 170 -0.1014 0.04805 1000 -2.11 0.0351 0.05 -0.1956 -0.00707
agec18*PersonID 171 0.1168 0.04449 1000 2.63 0.0088 0.05 0.02949 0.2041
agec18*PersonID 172 -0.07631 0.04603 1000 -1.66 0.0976 0.05 -0.1666 0.01401
agec18*PersonID 173 0.008134 0.04316 1000 0.19 0.8506 0.05 -0.07656 0.09283
agec18*PersonID 174 0.05003 0.04380 1000 1.14 0.2537 0.05 -0.03593 0.1360
agec18*PersonID 175 0.1167 0.04391 1000 2.66 0.0080 0.05 0.03056 0.2029
agec18*PersonID 176 -0.1016 0.04664 1000 -2.18 0.0296 0.05 -0.1931 -0.01006
agec18*PersonID 177 -0.01381 0.04461 1000 -0.31 0.7570 0.05 -0.1013 0.07372
agec18*PersonID 178 -0.02086 0.04327 1000 -0.48 0.6299 0.05 -0.1058 0.06406
agec18*PersonID 179 0.06388 0.04478 1000 1.43 0.1540 0.05 -0.02399 0.1518
agec18*PersonID 180 0.008941 0.04682 1000 0.19 0.8486 0.05 -0.08293 0.1008
agec18*PersonID 181 0.2641 0.04610 1000 5.73 <.0001 0.05 0.1736 0.3545
agec18*PersonID 182 0.03915 0.04504 1000 0.87 0.3849 0.05 -0.04924 0.1275
agec18*PersonID 183 -0.07140 0.04369 1000 -1.63 0.1026 0.05 -0.1571 0.01434
agec18*PersonID 184 0.05204 0.04740 1000 1.10 0.2725 0.05 -0.04097 0.1450
agec18*PersonID 185 -0.05439 0.04571 1000 -1.19 0.2344 0.05 -0.1441 0.03531
agec18*PersonID 186 0.1154 0.04613 1000 2.50 0.0125 0.05 0.02493 0.2060
agec18*PersonID 187 0.02025 0.04806 1000 0.42 0.6736 0.05 -0.07406 0.1146
agec18*PersonID 188 0.1860 0.04467 1000 4.16 <.0001 0.05 0.09832 0.2736
agec18*PersonID 189 0.1138 0.04537 1000 2.51 0.0123 0.05 0.02475 0.2028
agec18*PersonID 190 -0.01173 0.04694 1000 -0.25 0.8027 0.05 -0.1038 0.08038
agec18*PersonID 191 0.04334 0.04574 1000 0.95 0.3436 0.05 -0.04642 0.1331
agec18*PersonID 192 0.1189 0.04589 1000 2.59 0.0097 0.05 0.02884 0.2089
agec18*PersonID 193 -0.01888 0.04343 1000 -0.43 0.6640 0.05 -0.1041 0.06636
agec18*PersonID 194 -0.06564 0.04767 1000 -1.38 0.1688 0.05 -0.1592 0.02790
agec18*PersonID 195 -0.04258 0.04489 1000 -0.95 0.3432 0.05 -0.1307 0.04552
agec18*PersonID 196 -0.2361 0.04505 1000 -5.24 <.0001 0.05 -0.3245 -0.1477
agec18*PersonID 197 0.01973 0.04604 1000 0.43 0.6683 0.05 -0.07062 0.1101
agec18*PersonID 198 -0.1429 0.04646 1000 -3.08 0.0021 0.05 -0.2341 -0.05178
agec18*PersonID 199 -0.01913 0.04629 1000 -0.41 0.6795 0.05 -0.1100 0.07170
agec18*PersonID 200 0.02654 0.04723 1000 0.56 0.5743 0.05 -0.06614 0.1192

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
PersonID 200 1000 72147.6 360.74 <.0001 <.0001
agec18*PersonID 200 1000 1302.27 6.51 <.0001 <.0001


Descriptives for Fixed Effects and Residuals

The MEANS Procedure

Variable N Mean Variance Minimum Maximum
monGint
monGage
monGres
1400
1400
1400
0.0644042
-0.0035024
1.644716E-16
0.2330850
0.0134169
0.0577120
-1.7176614
-0.2714446
-0.7430323
1.3026619
0.3494314
0.7485458


Ch 9 Eq. 9.6: Predicting Risky Behavior from Monitoring Fixed Effects and Residuals

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 10
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8066.68413843  
1 2 7430.97530935 0.00000000

Convergence criteria met.

Estimated G Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2
1 Intercept 1 15.6164 1.8007
2 agec18 1 1.8007 0.4982

Estimated G Correlation Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2
1 Intercept 1 1.0000 0.6456
2 agec18 1 0.6456 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 15.6164 1.9099 8.18 <.0001
UN(2,1) PersonID 1.8007 0.3277 5.49 <.0001
UN(2,2) PersonID 0.4982 0.07695 6.47 <.0001
Residual   7.3316 0.3279 22.36 <.0001

Fit Statistics
-2 Log Likelihood 7431.0
AIC (Smaller is Better) 7459.0
AICC (Smaller is Better) 7459.3
BIC (Smaller is Better) 7505.2

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 635.71 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7431.0 14 7459.0 7459.3 7477.7 7505.2 7519.2

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.5535 0.3288 244 71.64 <.0001 0.05 22.9058 24.2011
agec18 1.9921 0.1383 1187 14.40 <.0001 0.05 1.7207 2.2635
agec18*agec18 0.1465 0.02058 1010 7.12 <.0001 0.05 0.1062 0.1869
att4 -3.3289 0.5140 200 -6.48 <.0001 0.05 -4.3425 -2.3153
agec18*att4 -0.5279 0.1027 199 -5.14 <.0001 0.05 -0.7305 -0.3254
monGint -3.3480 0.6468 199 -5.18 <.0001 0.05 -4.6234 -2.0726
agec18*monGint -0.3100 0.1291 198 -2.40 0.0173 0.05 -0.5647 -0.05542
monGage 7.4746 2.6945 200 2.77 0.0061 0.05 2.1613 12.7878
agec18*monGage -0.3919 0.5386 200 -0.73 0.4677 0.05 -1.4541 0.6702
monGres 3.5571 0.3013 1000 11.80 <.0001 0.05 2.9657 4.1484

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1187 207.38 207.38 <.0001 <.0001
agec18*agec18 1 1010 50.70 50.70 <.0001 <.0001
att4 1 200 41.94 41.94 <.0001 <.0001
agec18*att4 1 199 26.41 26.41 <.0001 <.0001
monGint 1 199 26.80 26.80 <.0001 <.0001
agec18*monGint 1 198 5.77 5.77 0.0163 0.0173
monGage 1 200 7.70 7.70 0.0055 0.0061
agec18*monGage 1 200 0.53 0.53 0.4668 0.4677
monGres 1 1000 139.33 139.33 <.0001 <.0001


Total R2 (% Reduction) for PredAttOnly vs. PredMonFixed

Name PredCorr TotalR2 TotalR2Diff
PredAttOnly 0.48557 0.23578 .
PredMonFixed 0.58062 0.33712 0.10134


Ch 9 Eq. 9.6: Predicting Risky Behavior from Monitoring Fixed Effects and Residuals
Adding Random Effect of WP Monitoring Residual

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 7
Columns in X 10
Columns in Z per Subject 3
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8066.68413843  
1 2 7482.54715701 139.97532034
2 1 7421.07386506 0.00094473
3 3 7419.17805335 .
4 1 7419.15331856 0.00000001
5 1 7419.15329344 0.00000000

Convergence criteria met.

Estimated G Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2 Col3
1 Intercept 1 15.6354 1.8036 3.4260
2 agec18 1 1.8036 0.4990 0.03176
3 monGres 1 3.4260 0.03176 0.1376

Estimated G Correlation Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2 Col3
1 Intercept 1 1.0000 0.6457 1.0000
2 agec18 1 0.6457 1.0000 0.1212
3 monGres 1 1.0000 0.1212 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 15.6354 1.9134 8.17 <.0001
UN(2,1) PersonID 1.8036 0.3283 5.49 <.0001
UN(2,2) PersonID 0.4990 0.07713 6.47 <.0001
UN(3,1) PersonID 3.4260 1.3550 2.53 0.0115
UN(3,2) PersonID 0.03176 0.2757 0.12 0.9083
UN(3,3) PersonID 0.1376 1.5472 0.09 0.4646
Residual   7.3220 0.3490 20.98 <.0001

Fit Statistics
-2 Log Likelihood 7419.2
AIC (Smaller is Better) 7453.2
AICC (Smaller is Better) 7453.6
BIC (Smaller is Better) 7509.2

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
6 647.53 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7419.2 17 7453.2 7453.6 7475.8 7509.2 7526.2

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.5582 0.3284 244 71.74 <.0001 0.05 22.9114 24.2050
agec18 1.9930 0.1377 1185 14.48 <.0001 0.05 1.7229 2.2631
agec18*agec18 0.1467 0.02046 1009 7.17 <.0001 0.05 0.1065 0.1868
att4 -3.1941 0.5059 200 -6.31 <.0001 0.05 -4.1916 -2.1966
agec18*att4 -0.5268 0.1027 199 -5.13 <.0001 0.05 -0.7294 -0.3242
monGint -3.3235 0.6362 200 -5.22 <.0001 0.05 -4.5779 -2.0690
agec18*monGint -0.3097 0.1292 198 -2.40 0.0174 0.05 -0.5644 -0.05496
monGage 7.1496 2.6528 201 2.70 0.0076 0.05 1.9187 12.3805
agec18*monGage -0.3933 0.5388 200 -0.73 0.4662 0.05 -1.4557 0.6690
monGres 3.5258 0.2989 156 11.80 <.0001 0.05 2.9353 4.1162

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1185 209.55 209.55 <.0001 <.0001
agec18*agec18 1 1009 51.40 51.40 <.0001 <.0001
att4 1 200 39.87 39.87 <.0001 <.0001
agec18*att4 1 199 26.29 26.29 <.0001 <.0001
monGint 1 200 27.29 27.29 <.0001 <.0001
agec18*monGint 1 198 5.75 5.75 0.0165 0.0174
monGage 1 201 7.26 7.26 0.0070 0.0076
agec18*monGage 1 200 0.53 0.53 0.4653 0.4662
monGres 1 156 139.14 139.14 <.0001 <.0001


Likelihood Ratio Test for FitGWPasfixed vs. FitGWPasrandom

Name Neg2LogLike Parms AIC BIC DevDiff DFdiff Pvalue
FitGWPasfixed 7431.0 14 7459.0 7505.2 . . .
FitGWPasrandom 7419.2 17 7453.2 7509.2 11.8220 3 .008018405


Ch 9 Eq. 9.6: Predicting Risky Behavior from Monitoring Fixed Effects and Residuals
Adding WP Monitoring by Age

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER9
Dependent Variable risky
Covariance Structure Unstructured
Subject Effect PersonID
Estimation Method ML
Residual Variance Method Profile
Fixed Effects SE Method Model-Based
Degrees of Freedom Method Satterthwaite

Dimensions
Covariance Parameters 4
Columns in X 11
Columns in Z per Subject 2
Subjects 200
Max Obs per Subject 7

Number of Observations
Number of Observations Read 1400
Number of Observations Used 1400
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 8062.50313632  
1 2 7420.34633824 0.00000000

Convergence criteria met.

Estimated G Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2
1 Intercept 1 15.6361 1.8061
2 agec18 1 1.8061 0.5010

Estimated G Correlation Matrix
Row Effect PersonID: Person
ID Number		 Col1 Col2
1 Intercept 1 1.0000 0.6453
2 agec18 1 0.6453 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 15.6361 1.9080 8.20 <.0001
UN(2,1) PersonID 1.8061 0.3274 5.52 <.0001
UN(2,2) PersonID 0.5010 0.07692 6.51 <.0001
Residual   7.2537 0.3244 22.36 <.0001

Fit Statistics
-2 Log Likelihood 7420.3
AIC (Smaller is Better) 7450.3
AICC (Smaller is Better) 7450.7
BIC (Smaller is Better) 7499.8

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 642.16 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
7420.3 15 7450.3 7450.7 7470.4 7499.8 7514.8

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 23.5540 0.3285 244 71.70 <.0001 0.05 22.9069 24.2010
agec18 1.9870 0.1378 1186 14.42 <.0001 0.05 1.7167 2.2573
agec18*agec18 0.1453 0.02048 1009 7.10 <.0001 0.05 0.1051 0.1855
att4 -3.3020 0.5139 200 -6.43 <.0001 0.05 -4.3153 -2.2887
agec18*att4 -0.5189 0.1028 200 -5.05 <.0001 0.05 -0.7215 -0.3163
monGint -3.4000 0.6467 199 -5.26 <.0001 0.05 -4.6752 -2.1248
agec18*monGint -0.3274 0.1292 199 -2.53 0.0121 0.05 -0.5823 -0.07258
monGage 7.4327 2.6933 200 2.76 0.0063 0.05 2.1217 12.7437
agec18*monGage -0.4056 0.5386 200 -0.75 0.4524 0.05 -1.4677 0.6566
monGres 5.3253 0.6184 1100 8.61 <.0001 0.05 4.1119 6.5387
agec18*monGres 0.5816 0.1779 1126 3.27 0.0011 0.05 0.2325 0.9307

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
agec18 1 1186 208.04 208.04 <.0001 <.0001
agec18*agec18 1 1009 50.37 50.37 <.0001 <.0001
att4 1 200 41.29 41.29 <.0001 <.0001
agec18*att4 1 200 25.50 25.50 <.0001 <.0001
monGint 1 199 27.64 27.64 <.0001 <.0001
agec18*monGint 1 199 6.42 6.42 0.0113 0.0121
monGage 1 200 7.62 7.62 0.0058 0.0063
agec18*monGage 1 200 0.57 0.57 0.4515 0.4524
monGres 1 1100 74.15 74.15 <.0001 <.0001
agec18*monGres 1 1126 10.69 10.69 0.0011 0.0011