Chapter 7a: Descriptive Statistics for Time-Invariant Variables

The MEANS Procedure

Variable Label N Mean Std Dev Minimum Maximum
PMwomen
PMbaseage
women: 0=Men, 1=Women
baseage: Age at Baseline
105
105
0.7333333
80.1296698
0.4443376
6.1050092
0
69.7029432
1.0000000
95.3073238


Chapter 7a: Descriptive Statistics for Time-Varying Variables

The MEANS Procedure

Analysis Variable : symptoms symptoms: # Daily Physical Symptoms (0=5)
N Mean Std Dev Minimum Maximum
509 1.2730845 1.3243935 0 5.0000000


Eq 7a.3: Empty Means, Random Intercept Model

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
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 105
Max Obs per Subject 5

Number of Observations
Number of Observations Read 509
Number of Observations Used 509
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 1729.49026844  
1 2 1443.00619475 0.00015381
2 1 1442.96582713 0.00000044
3 1 1442.96571502 0.00000000

Convergence criteria met.

Estimated V Matrix for PersonID 102
Row Col1 Col2 Col3 Col4 Col5
1 1.8055 1.1893 1.1893 1.1893 1.1893
2 1.1893 1.8055 1.1893 1.1893 1.1893
3 1.1893 1.1893 1.8055 1.1893 1.1893
4 1.1893 1.1893 1.1893 1.8055 1.1893
5 1.1893 1.1893 1.1893 1.1893 1.8055

Estimated V Correlation Matrix for PersonID 102
Row Col1 Col2 Col3 Col4 Col5
1 1.0000 0.6587 0.6587 0.6587 0.6587
2 0.6587 1.0000 0.6587 0.6587 0.6587
3 0.6587 0.6587 1.0000 0.6587 0.6587
4 0.6587 0.6587 0.6587 1.0000 0.6587
5 0.6587 0.6587 0.6587 0.6587 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr > Z
UN(1,1) PersonID 1.1893 0.1839 6.47 <.0001
Residual   0.6162 0.04346 14.18 <.0001

Fit Statistics
-2 Log Likelihood 1443.0
AIC (Smaller is Better) 1449.0
AICC (Smaller is Better) 1449.0
BIC (Smaller is Better) 1456.9

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 286.52 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1443.0 3 1449.0 1449.0 1452.2 1456.9 1459.9

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 1.2940 0.1120 103 11.55 <.0001 0.05 1.0718 1.5162


Ch 7a: Testing Saturated Means by Day of Study
Random Intercept Only

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
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 15
Columns in Z per Subject 1
Subjects 105
Max Obs per Subject 5

Number of Observations
Number of Observations Read 509
Number of Observations Used 509
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 1719.01546687  
1 2 1435.11964594 0.00047879
2 1 1434.99322293 0.00000404
3 1 1434.99220899 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr > Z
UN(1,1) PersonID 1.1812 0.1829 6.46 <.0001
Residual   0.6054 0.04273 14.17 <.0001

Fit Statistics
-2 Log Likelihood 1435.0
AIC (Smaller is Better) 1467.0
AICC (Smaller is Better) 1468.1
BIC (Smaller is Better) 1509.5

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 284.02 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1435.0 16 1467.0 1468.1 1484.2 1509.5 1525.5

Solution for Fixed Effects
Effect studyday:
Actual
Day in
Study (1-14)		 Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept   1.0276 0.2555 509 4.02 <.0001 0.05 0.5257 1.5296
studyday 1 0.2593 0.2446 422 1.06 0.2897 0.05 -0.2214 0.7400
studyday 2 0.3242 0.2577 423 1.26 0.2091 0.05 -0.1824 0.8308
studyday 3 0.1950 0.2782 423 0.70 0.4836 0.05 -0.3517 0.7418
studyday 4 0.4473 0.2995 425 1.49 0.1360 0.05 -0.1413 1.0359
studyday 5 0.3005 0.2918 426 1.03 0.3036 0.05 -0.2730 0.8740
studyday 6 0.3771 0.2710 426 1.39 0.1648 0.05 -0.1555 0.9097
studyday 7 0.3139 0.2565 422 1.22 0.2217 0.05 -0.1903 0.8182
studyday 8 0.2359 0.2568 425 0.92 0.3588 0.05 -0.2689 0.7408
studyday 9 0.07920 0.2729 421 0.29 0.7718 0.05 -0.4571 0.6156
studyday 10 0.4512 0.2917 422 1.55 0.1227 0.05 -0.1222 1.0246
studyday 11 -0.04604 0.3576 422 -0.13 0.8976 0.05 -0.7490 0.6569
studyday 12 0.2235 0.3316 422 0.67 0.5007 0.05 -0.4283 0.8754
studyday 13 0.1685 0.3170 412 0.53 0.5954 0.05 -0.4548 0.7917
studyday 14 0 . . . . . . .

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
studyday 13 416 8.04 0.62 0.8410 0.8391

Least Squares Means
Effect studyday:
Actual
Day in
Study (1-14)		 Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
studyday 1 1.2869 0.1307 187 9.85 <.0001 0.05 1.0291 1.5448
studyday 2 1.3519 0.1507 289 8.97 <.0001 0.05 1.0553 1.6484
studyday 3 1.2227 0.1842 428 6.64 <.0001 0.05 0.8606 1.5848
studyday 4 1.4749 0.2091 483 7.05 <.0001 0.05 1.0641 1.8858
studyday 5 1.3281 0.1998 466 6.65 <.0001 0.05 0.9356 1.7207
studyday 6 1.4047 0.1664 362 8.44 <.0001 0.05 1.0775 1.7319
studyday 7 1.3416 0.1522 297 8.81 <.0001 0.05 1.0420 1.6412
studyday 8 1.2636 0.1467 269 8.61 <.0001 0.05 0.9747 1.5524
studyday 9 1.1068 0.1781 409 6.22 <.0001 0.05 0.7568 1.4569
studyday 10 1.4788 0.2061 477 7.18 <.0001 0.05 1.0739 1.8837
studyday 11 0.9816 0.2906 504 3.38 0.0008 0.05 0.4107 1.5525
studyday 12 1.2512 0.2598 509 4.82 <.0001 0.05 0.7407 1.7616
studyday 13 1.1961 0.2598 509 4.60 <.0001 0.05 0.6856 1.7065
studyday 14 1.0276 0.2555 509 4.02 <.0001 0.05 0.5257 1.5296

Differences of Least Squares Means
Effect studyday:
Actual
Day in
Study (1-14)		 studyday:
Actual
Day in
Study (1-14)		 Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
studyday 1 2 -0.06493 0.1314 407 -0.49 0.6214 0.05 -0.3232 0.1933
studyday 1 3 0.06426 0.1688 413 0.38 0.7036 0.05 -0.2675 0.3960
studyday 1 4 -0.1880 0.1956 415 -0.96 0.3371 0.05 -0.5726 0.1966
studyday 1 5 -0.04120 0.1861 417 -0.22 0.8249 0.05 -0.4070 0.3246
studyday 1 6 -0.1178 0.1495 412 -0.79 0.4312 0.05 -0.4116 0.1760
studyday 1 7 -0.05464 0.1334 409 -0.41 0.6823 0.05 -0.3169 0.2076
studyday 1 8 0.02336 0.1268 407 0.18 0.8539 0.05 -0.2259 0.2727
studyday 1 9 0.1801 0.1620 412 1.11 0.2670 0.05 -0.1384 0.4986
studyday 1 10 -0.1919 0.1924 417 -1.00 0.3191 0.05 -0.5700 0.1862
studyday 1 11 0.3053 0.2810 419 1.09 0.2779 0.05 -0.2471 0.8578
studyday 1 12 0.03577 0.2492 419 0.14 0.8859 0.05 -0.4540 0.5255
studyday 1 13 0.09083 0.2491 419 0.36 0.7156 0.05 -0.3988 0.5805
studyday 1 14 0.2593 0.2446 422 1.06 0.2897 0.05 -0.2214 0.7400
studyday 2 3 0.1292 0.1833 412 0.70 0.4814 0.05 -0.2312 0.4896
studyday 2 4 -0.1231 0.2083 414 -0.59 0.5549 0.05 -0.5326 0.2864
studyday 2 5 0.02373 0.2042 420 0.12 0.9075 0.05 -0.3776 0.4251
studyday 2 6 -0.05283 0.1683 415 -0.31 0.7537 0.05 -0.3836 0.2780
studyday 2 7 0.01029 0.1533 411 0.07 0.9465 0.05 -0.2911 0.3117
studyday 2 8 0.08830 0.1473 410 0.60 0.5492 0.05 -0.2012 0.3778
studyday 2 9 0.2450 0.1779 412 1.38 0.1691 0.05 -0.1047 0.5947
studyday 2 10 -0.1270 0.2074 418 -0.61 0.5408 0.05 -0.5346 0.2807
studyday 2 11 0.3703 0.2920 419 1.27 0.2054 0.05 -0.2036 0.9442
studyday 2 12 0.1007 0.2636 422 0.38 0.7026 0.05 -0.4175 0.6189
studyday 2 13 0.1558 0.2619 420 0.59 0.5523 0.05 -0.3590 0.6705
studyday 2 14 0.3242 0.2577 423 1.26 0.2091 0.05 -0.1824 0.8308
studyday 3 4 -0.2523 0.2310 413 -1.09 0.2753 0.05 -0.7063 0.2017
studyday 3 5 -0.1055 0.2307 422 -0.46 0.6478 0.05 -0.5589 0.3480
studyday 3 6 -0.1820 0.2039 422 -0.89 0.3726 0.05 -0.5829 0.2189
studyday 3 7 -0.1189 0.1874 416 -0.63 0.5260 0.05 -0.4872 0.2494
studyday 3 8 -0.04090 0.1831 416 -0.22 0.8234 0.05 -0.4008 0.3190
studyday 3 9 0.1158 0.2066 414 0.56 0.5753 0.05 -0.2903 0.5220
studyday 3 10 -0.2562 0.2316 418 -1.11 0.2694 0.05 -0.7115 0.1991
studyday 3 11 0.2411 0.3057 416 0.79 0.4308 0.05 -0.3599 0.8421
studyday 3 12 -0.02849 0.2854 423 -0.10 0.9205 0.05 -0.5894 0.5324
studyday 3 13 0.02657 0.2847 422 0.09 0.9257 0.05 -0.5331 0.5863
studyday 3 14 0.1950 0.2782 423 0.70 0.4836 0.05 -0.3517 0.7418
studyday 4 5 0.1468 0.2432 415 0.60 0.5464 0.05 -0.3312 0.6249
studyday 4 6 0.07026 0.2238 419 0.31 0.7537 0.05 -0.3697 0.5102
studyday 4 7 0.1334 0.2133 418 0.63 0.5320 0.05 -0.2858 0.5526
studyday 4 8 0.2114 0.2088 418 1.01 0.3120 0.05 -0.1991 0.6218
studyday 4 9 0.3681 0.2332 419 1.58 0.1152 0.05 -0.09030 0.8265
studyday 4 10 -0.00388 0.2533 419 -0.02 0.9878 0.05 -0.5017 0.4940
studyday 4 11 0.4934 0.3204 416 1.54 0.1244 0.05 -0.1365 1.1232
studyday 4 12 0.2238 0.3026 422 0.74 0.4599 0.05 -0.3709 0.8185
studyday 4 13 0.2789 0.3014 422 0.93 0.3555 0.05 -0.3137 0.8714
studyday 4 14 0.4473 0.2995 425 1.49 0.1360 0.05 -0.1413 1.0359
studyday 5 6 -0.07656 0.2088 413 -0.37 0.7141 0.05 -0.4871 0.3340
studyday 5 7 -0.01344 0.2011 416 -0.07 0.9468 0.05 -0.4088 0.3819
studyday 5 8 0.06457 0.1985 418 0.33 0.7452 0.05 -0.3257 0.4548
studyday 5 9 0.2213 0.2253 421 0.98 0.3265 0.05 -0.2215 0.6641
studyday 5 10 -0.1507 0.2504 425 -0.60 0.5475 0.05 -0.6428 0.3414
studyday 5 11 0.3465 0.3220 422 1.08 0.2825 0.05 -0.2864 0.9795
studyday 5 12 0.07697 0.2824 413 0.27 0.7853 0.05 -0.4782 0.6321
studyday 5 13 0.1320 0.2951 423 0.45 0.6548 0.05 -0.4481 0.7121
studyday 5 14 0.3005 0.2918 426 1.03 0.3036 0.05 -0.2730 0.8740
studyday 6 7 0.06312 0.1677 412 0.38 0.7067 0.05 -0.2665 0.3927
studyday 6 8 0.1411 0.1616 410 0.87 0.3831 0.05 -0.1766 0.4588
studyday 6 9 0.2979 0.1952 418 1.53 0.1278 0.05 -0.08588 0.6816
studyday 6 10 -0.07414 0.2219 422 -0.33 0.7385 0.05 -0.5103 0.3621
studyday 6 11 0.4231 0.3024 422 1.40 0.1624 0.05 -0.1712 1.0174
studyday 6 12 0.1535 0.2680 417 0.57 0.5670 0.05 -0.3732 0.6803
studyday 6 13 0.2086 0.2691 419 0.78 0.4387 0.05 -0.3203 0.7375
studyday 6 14 0.3771 0.2710 426 1.39 0.1648 0.05 -0.1555 0.9097
studyday 7 8 0.07800 0.1475 408 0.53 0.5972 0.05 -0.2119 0.3679
studyday 7 9 0.2347 0.1820 416 1.29 0.1979 0.05 -0.1230 0.5925
studyday 7 10 -0.1373 0.2115 421 -0.65 0.5167 0.05 -0.5530 0.2785
studyday 7 11 0.3600 0.2959 422 1.22 0.2245 0.05 -0.2216 0.9416
studyday 7 12 0.09041 0.2622 419 0.34 0.7304 0.05 -0.4249 0.6057
studyday 7 13 0.1455 0.2633 420 0.55 0.5810 0.05 -0.3721 0.6631
studyday 7 14 0.3139 0.2565 422 1.22 0.2217 0.05 -0.1903 0.8182
studyday 8 9 0.1567 0.1766 415 0.89 0.3753 0.05 -0.1904 0.5039
studyday 8 10 -0.2153 0.2054 419 -1.05 0.2953 0.05 -0.6190 0.1885
studyday 8 11 0.2820 0.2921 421 0.97 0.3350 0.05 -0.2923 0.8562
studyday 8 12 0.01241 0.2584 419 0.05 0.9617 0.05 -0.4955 0.5203
studyday 8 13 0.06747 0.2612 421 0.26 0.7963 0.05 -0.4460 0.5810
studyday 8 14 0.2359 0.2568 425 0.92 0.3588 0.05 -0.2689 0.7408
studyday 9 10 -0.3720 0.2256 416 -1.65 0.0999 0.05 -0.8154 0.07142
studyday 9 11 0.1252 0.3046 418 0.41 0.6811 0.05 -0.4734 0.7239
studyday 9 12 -0.1443 0.2788 421 -0.52 0.6049 0.05 -0.6923 0.4036
studyday 9 13 -0.08927 0.2769 419 -0.32 0.7473 0.05 -0.6336 0.4551
studyday 9 14 0.07920 0.2729 421 0.29 0.7718 0.05 -0.4571 0.6156
studyday 10 11 0.4972 0.3206 418 1.55 0.1216 0.05 -0.1329 1.1274
studyday 10 12 0.2277 0.3014 425 0.76 0.4504 0.05 -0.3647 0.8201
studyday 10 13 0.2827 0.2935 419 0.96 0.3360 0.05 -0.2942 0.8597
studyday 10 14 0.4512 0.2917 422 1.55 0.1227 0.05 -0.1222 1.0246
studyday 11 12 -0.2696 0.3612 422 -0.75 0.4559 0.05 -0.9795 0.4404
studyday 11 13 -0.2145 0.3604 421 -0.60 0.5521 0.05 -0.9230 0.4940
studyday 11 14 -0.04604 0.3576 422 -0.13 0.8976 0.05 -0.7490 0.6569
studyday 12 13 0.05506 0.3370 421 0.16 0.8703 0.05 -0.6074 0.7175
studyday 12 14 0.2235 0.3316 422 0.67 0.5007 0.05 -0.4283 0.8754
studyday 13 14 0.1685 0.3170 412 0.53 0.5954 0.05 -0.4548 0.7917


Ch 7a: Testing Fixed Linear Effect of Day of Study
Random Intercept Only

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
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 105
Max Obs per Subject 5

Number of Observations
Number of Observations Read 509
Number of Observations Used 509
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 1729.28821723  
1 2 1442.19695741 0.00015522
2 1 1442.15628017 0.00000045
3 1 1442.15616639 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr > Z
UN(1,1) PersonID 1.1900 0.1840 6.47 <.0001
Residual   0.6149 0.04337 14.18 <.0001

Fit Statistics
-2 Log Likelihood 1442.2
AIC (Smaller is Better) 1450.2
AICC (Smaller is Better) 1450.2
BIC (Smaller is Better) 1460.8

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 287.13 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1442.2 4 1450.2 1450.2 1454.5 1460.8 1464.8

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 1.3351 0.1210 139 11.03 <.0001 0.05 1.0958 1.5744
studyday1 -0.00893 0.009920 413 -0.90 0.3685 0.05 -0.02843 0.01057

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
studyday1 1 413 0.81 0.81 0.3680 0.3685


Ch 7a: Testing Random Linear Effect of Day of Study

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
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 105
Max Obs per Subject 5

Number of Observations
Number of Observations Read 509
Number of Observations Used 509
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 1729.28821723  
1 2 1440.66175354 0.00168545
2 1 1440.18892960 0.00006245
3 1 1440.17275925 0.00000010
4 1 1440.17273352 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 1.2037 0.2157 5.58 <.0001
UN(2,1) PersonID -0.00502 0.01484 -0.34 0.7352
UN(2,2) PersonID 0.002000 0.001660 1.20 0.1142
Residual   0.5835 0.04641 12.57 <.0001

Fit Statistics
-2 Log Likelihood 1440.2
AIC (Smaller is Better) 1452.2
AICC (Smaller is Better) 1452.3
BIC (Smaller is Better) 1468.1

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
3 289.12 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1440.2 6 1452.2 1452.3 1458.6 1468.1 1474.1

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 1.3334 0.1210 102 11.02 <.0001 0.05 1.0933 1.5734
studyday1 -0.00837 0.01081 92.2 -0.77 0.4406 0.05 -0.02985 0.01310

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
studyday1 1 92.2 0.60 0.60 0.4386 0.4406


Likelihood Ratio Test for FitFixDayofStudy vs. FitRandDayofStudy

Name Neg2LogLike Parms AIC BIC DevDiff DFdiff Pvalue
FitFixDayofStudy 1442.2 4 1450.2 1460.8 . . .
FitRandDayofStudy 1440.2 6 1452.2 1468.1 1.98343 2 0.37094


Ch 7a: Testing Saturated Means by Day of Week
Random Intercept Only

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
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 8
Columns in Z per Subject 1
Subjects 105
Max Obs per Subject 5

Number of Observations
Number of Observations Read 509
Number of Observations Used 509
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 1726.70946135  
1 2 1442.37413805 0.00027950
2 1 1442.30011142 0.00000143
3 1 1442.29974858 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr > Z
UN(1,1) PersonID 1.1891 0.1841 6.46 <.0001
Residual   0.6152 0.04341 14.17 <.0001

Fit Statistics
-2 Log Likelihood 1442.3
AIC (Smaller is Better) 1460.3
AICC (Smaller is Better) 1460.7
BIC (Smaller is Better) 1484.2

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 284.41 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1442.3 9 1460.3 1460.7 1470.0 1484.2 1493.2

Solution for Fixed Effects
Effect dayofweek:
1=Monday
to 7=Sunday		 Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept   1.2650 0.1373 216 9.21 <.0001 0.05 0.9944 1.5356
dayofweek 1 0.07211 0.1435 415 0.50 0.6156 0.05 -0.2100 0.3542
dayofweek 2 0.05735 0.1764 419 0.33 0.7452 0.05 -0.2893 0.4040
dayofweek 3 0.02929 0.1718 428 0.17 0.8647 0.05 -0.3084 0.3670
dayofweek 4 0.08723 0.1455 419 0.60 0.5491 0.05 -0.1987 0.3732
dayofweek 5 0.02039 0.1337 416 0.15 0.8788 0.05 -0.2423 0.2831
dayofweek 6 0.009694 0.1065 411 0.09 0.9275 0.05 -0.1996 0.2190
dayofweek 7 0 . . . . . . .

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
dayofweek 6 418 0.67 0.11 0.9952 0.9951

Least Squares Means
Effect dayofweek:
1=Monday
to 7=Sunday		 Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
dayofweek 1 1.3371 0.1572 316 8.50 <.0001 0.05 1.0278 1.6464
dayofweek 2 1.3224 0.1864 430 7.09 <.0001 0.05 0.9560 1.6887
dayofweek 3 1.2943 0.1781 400 7.27 <.0001 0.05 0.9441 1.6445
dayofweek 4 1.3522 0.1559 310 8.68 <.0001 0.05 1.0456 1.6589
dayofweek 5 1.2854 0.1458 260 8.81 <.0001 0.05 0.9983 1.5725
dayofweek 6 1.2747 0.1229 147 10.37 <.0001 0.05 1.0318 1.5176
dayofweek 7 1.2650 0.1373 216 9.21 <.0001 0.05 0.9944 1.5356

Differences of Least Squares Means
Effect dayofweek:
1=Monday
to 7=Sunday		 dayofweek:
1=Monday
to 7=Sunday		 Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
dayofweek 1 2 0.01475 0.1887 416 0.08 0.9377 0.05 -0.3561 0.3856
dayofweek 1 3 0.04282 0.1904 430 0.22 0.8222 0.05 -0.3314 0.4170
dayofweek 1 4 -0.01512 0.1678 425 -0.09 0.9282 0.05 -0.3450 0.3147
dayofweek 1 5 0.05172 0.1555 421 0.33 0.7396 0.05 -0.2540 0.3574
dayofweek 1 6 0.06241 0.1318 417 0.47 0.6360 0.05 -0.1966 0.3214
dayofweek 1 7 0.07211 0.1435 415 0.50 0.6156 0.05 -0.2100 0.3542
dayofweek 2 3 0.02807 0.2099 424 0.13 0.8937 0.05 -0.3844 0.4406
dayofweek 2 4 -0.02987 0.1945 423 -0.15 0.8780 0.05 -0.4121 0.3524
dayofweek 2 5 0.03696 0.1872 424 0.20 0.8436 0.05 -0.3311 0.4050
dayofweek 2 6 0.04766 0.1662 420 0.29 0.7744 0.05 -0.2790 0.3743
dayofweek 2 7 0.05735 0.1764 419 0.33 0.7452 0.05 -0.2893 0.4040
dayofweek 3 4 -0.05794 0.1810 419 -0.32 0.7490 0.05 -0.4137 0.2978
dayofweek 3 5 0.008899 0.1752 422 0.05 0.9595 0.05 -0.3354 0.3532
dayofweek 3 6 0.01959 0.1562 423 0.13 0.9003 0.05 -0.2875 0.3267
dayofweek 3 7 0.02929 0.1718 428 0.17 0.8647 0.05 -0.3084 0.3670
dayofweek 4 5 0.06684 0.1516 415 0.44 0.6594 0.05 -0.2311 0.3647
dayofweek 4 6 0.07753 0.1294 414 0.60 0.5495 0.05 -0.1769 0.3320
dayofweek 4 7 0.08723 0.1455 419 0.60 0.5491 0.05 -0.1987 0.3732
dayofweek 5 6 0.01069 0.1171 411 0.09 0.9273 0.05 -0.2195 0.2408
dayofweek 5 7 0.02039 0.1337 416 0.15 0.8788 0.05 -0.2423 0.2831
dayofweek 6 7 0.009694 0.1065 411 0.09 0.9275 0.05 -0.1996 0.2190


Ch 7a: Testing Fixed Effect of Weekend
Random Intercept Only

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
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 105
Max Obs per Subject 5

Number of Observations
Number of Observations Read 509
Number of Observations Used 509
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 1729.47233849  
1 2 1442.60770019 0.00016798
2 1 1442.56359005 0.00000052
3 1 1442.56345686 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr > Z
UN(1,1) PersonID 1.1914 0.1842 6.47 <.0001
Residual   0.6153 0.04340 14.18 <.0001

Fit Statistics
-2 Log Likelihood 1442.6
AIC (Smaller is Better) 1450.6
AICC (Smaller is Better) 1450.6
BIC (Smaller is Better) 1461.2

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 286.91 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1442.6 4 1450.6 1450.6 1454.9 1461.2 1465.2

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 1.3176 0.1182 126 11.15 <.0001 0.05 1.0838 1.5514
weekend -0.04642 0.07314 411 -0.63 0.5260 0.05 -0.1902 0.09736

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
weekend 1 411 0.40 0.40 0.5257 0.5260


Ch 7a: Testing Random Effect of Weekend

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
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 105
Max Obs per Subject 5

Number of Observations
Number of Observations Read 509
Number of Observations Used 509
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 1729.47233849  
1 4 1443.03699104 0.00287270
2 1 1442.25760525 0.00006141
3 1 1442.24171403 0.00000007
4 1 1442.24169734 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr Z
UN(1,1) PersonID 1.2468 0.2159 5.78 <.0001
UN(2,1) PersonID -0.05167 0.09121 -0.57 0.5710
UN(2,2) PersonID 0 . . .
Residual   0.6151 0.04337 14.18 <.0001

Fit Statistics
-2 Log Likelihood 1442.2
AIC (Smaller is Better) 1452.2
AICC (Smaller is Better) 1452.4
BIC (Smaller is Better) 1465.5

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
2 287.23 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1442.2 5 1452.2 1452.4 1457.6 1465.5 1470.5

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 1.3185 0.1204 98.7 10.95 <.0001 0.05 1.0797 1.5574
weekend -0.04676 0.07311 412 -0.64 0.5228 0.05 -0.1905 0.09695

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
weekend 1 412 0.41 0.41 0.5224 0.5228


Likelihood Ratio Test for FitFixWeekend vs. FitRandWeekend

Name Neg2LogLike Parms AIC BIC DevDiff DFdiff Pvalue
FitFixWeekend 1442.6 4 1450.6 1461.2 . . .
FitRandWeekend 1442.2 5 1452.2 1465.5 0.32176 1 0.57055


Eq 7a.4: Adding Sex and Age to the Model for the Means

The Mixed Procedure

Model Information
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
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 4
Columns in Z per Subject 1
Subjects 105
Max Obs per Subject 5

Number of Observations
Number of Observations Read 509
Number of Observations Used 509
Number of Observations Not Used 0

Iteration History
Iteration Evaluations -2 Log Like Criterion
0 1 1704.13890542  
1 2 1432.58060818 0.00001063
2 1 1432.57794163 0.00000000

Convergence criteria met.

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error Z Value Pr > Z
UN(1,1) PersonID 1.0726 0.1667 6.43 <.0001
Residual   0.6152 0.04333 14.20 <.0001

Fit Statistics
-2 Log Likelihood 1432.6
AIC (Smaller is Better) 1444.6
AICC (Smaller is Better) 1444.7
BIC (Smaller is Better) 1460.5

Null Model Likelihood Ratio Test
DF Chi-Square Pr > ChiSq
1 271.56 <.0001

Information Criteria
Neg2LogLike Parms AIC AICC HQIC BIC CAIC
1432.6 6 1444.6 1444.7 1451.0 1460.5 1466.5

Solution for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Intercept 1.7127 0.2108 106 8.13 <.0001 0.05 1.2949 2.1306
women -0.5306 0.2451 105 -2.16 0.0327 0.05 -1.0166 -0.04463
age80 0.10000 0.03706 108 2.70 0.0081 0.05 0.02655 0.1735
women*age80 -0.1104 0.04220 107 -2.62 0.0102 0.05 -0.1941 -0.02677

Covariance Matrix for Fixed Effects
Row Effect Col1 Col2 Col3 Col4
1 Intercept 0.04442 -0.04442 0.001315 -0.00131
2 women -0.04442 0.06008 -0.00131 0.001113
3 age80 0.001315 -0.00131 0.001373 -0.00137
4 women*age80 -0.00131 0.001113 -0.00137 0.001781

Type 3 Tests of Fixed Effects
Effect Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
women 1 105 4.69 4.69 0.0304 0.0327
age80 1 108 7.28 7.28 0.0070 0.0081
women*age80 1 107 6.85 6.85 0.0089 0.0102

Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Age Slope for Men 0.10000 0.03706 108 2.70 0.0081 0.05 0.02655 0.1735
Age Slope for Women -0.01043 0.02019 104 -0.52 0.6066 0.05 -0.05047 0.02961

Contrasts
Label Num DF Den DF Chi-Square F Value Pr > ChiSq Pr > F
Multivariate Test of Fixed Effects 3 106 10.85 3.62 0.0126 0.0156


Likelihood Ratio Test for FitEmpty vs. FitSexAge

Name Neg2LogLike Parms AIC BIC DevDiff DFdiff Pvalue
FitEmpty 1443.0 3 1449.0 1456.9 . . .
FitSexAge 1432.6 6 1444.6 1460.5 10.3878 3 0.015542


PsuedoR2 (% Reduction) for CovEmpty vs. CovSexAge

Name CovParm Subject Estimate StdErr ZValue ProbZ PseudoR2
CovEmpty UN(1,1) PersonID 1.1893 0.1839 6.47 <.0001 .
CovEmpty Residual   0.6162 0.04346 14.18 <.0001 .
CovSexAge UN(1,1) PersonID 1.0726 0.1667 6.43 <.0001 0.098100
CovSexAge Residual   0.6152 0.04333 14.20 <.0001 0.001578


Total R2 (% Reduction) for PredEmpty vs. PredSexAge

Name PredCorr TotalR2 TotalR2Diff
PredEmpty . . .
PredSexAge 0.22012 0.048451 .


Regions of significance for women*age80 interaction:
The effect of women will be significant at centered values of age80 BELOW the lower bound
and ABOVE the upper bound, which translate to these uncentered lower and upper bounds.

CenteredLower CenteredUpper UncenteredLower UncenteredUpper
-19.8358 -0.47803 60.1642 79.5220


Eq 7.4: Adding Sex and Age to the Model for the Means via NLMIXED

The NLMIXED Procedure

Specifications
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
Distribution for Dependent Variable Normal
Random Effects U0i
Distribution for Random Effects Normal
Subject Variable PersonID
Optimization Technique Newton-Raphson
Integration Method Adaptive Gaussian Quadrature

Dimensions
Observations Used 509
Observations Not Used 0
Total Observations 509
Subjects 105
Max Obs per Subject 5
Parameters 6
Quadrature Points 1

Parameters
fint fsex fage fsexage varEint vU0int NegLogLike
1.7 -0.53 0.1 -0.11 1 1 879.649573

Iteration History
Iter   Calls NegLogLike Diff MaxGrad Slope
1   20 751.828616 127.821 94.77571 -578.948
2   30 722.279882 29.54873 51.7238 -88.0735
3   40 716.34633 5.933552 4.812574 -11.1774
4   50 716.288978 0.057352 0.053967 -0.11386
5   60 716.288971 7.201E-6 6.859E-6 -0.00001

NOTE: ABSGCONV convergence criterion satisfied.

Fit Statistics
-2 Log Likelihood 1432.6
AIC (smaller is better) 1444.6
AICC (smaller is better) 1444.7
BIC (smaller is better) 1460.5

Parameter Estimates
Parameter Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper Gradient
fint 1.7127 0.2108 104 8.13 <.0001 0.05 1.2948 2.1307 2.173E-8
fsex -0.5306 0.2451 104 -2.16 0.0327 0.05 -1.0168 -0.04452 8.892E-9
fage 0.10000 0.03706 104 2.70 0.0081 0.05 0.02651 0.1735 2.541E-7
fsexage -0.1104 0.04220 104 -2.62 0.0102 0.05 -0.1941 -0.02673 1.586E-7
varEint -0.4858 0.07043 104 -6.90 <.0001 0.05 -0.6255 -0.3462 -6.86E-6
vU0int 0.07021 0.1554 104 0.45 0.6524 0.05 -0.2380 0.3784 6.588E-8

Contrasts
Label Num DF Den DF F Value Pr > F
Multivariate Test of Fixed Effects in Model for Means 3 104 3.61 0.0157

Additional Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Age Slope for Men 0.10000 0.03706 104 2.70 0.0081 0.05 0.02651 0.1735
Age Slope for Women -0.01043 0.02019 104 -0.52 0.6066 0.05 -0.05047 0.02961
Level-1 Residual Variance 0.6152 0.04333 104 14.20 <.0001 0.05 0.5293 0.7011
Level-2 Random Intercept Variance 1.0727 0.1667 104 6.43 <.0001 0.05 0.7421 1.4034


Eq 7.5: Adding Sex and Age to Predict Heterogeneity of Level-2 Variance

The NLMIXED Procedure

Specifications
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
Distribution for Dependent Variable Normal
Random Effects U0i
Distribution for Random Effects Normal
Subject Variable PersonID
Optimization Technique Newton-Raphson
Integration Method Adaptive Gaussian Quadrature

Dimensions
Observations Used 509
Observations Not Used 0
Total Observations 509
Subjects 105
Max Obs per Subject 5
Parameters 9
Quadrature Points 1

Parameters
fint fsex fage fsexage varEint vU0int vU0sex vU0age vU0sexage NegLogLike
1.7 -0.53 0.1 -0.11 1 1 0 0 0 879.649573

Iteration History
Iter   Calls NegLogLike Diff MaxGrad Slope
1   26 752.59613 127.0534 97.61735 -592.065
2   39 721.46655 31.12958 57.38355 -96.4522
3   52 714.184871 7.281679 5.762302 -13.6497
4   65 714.101468 0.083403 0.076605 -0.16536
5   78 714.101453 0.000015 0.000014 -0.00003
6   90 714.101453 2.27E-13 6.07E-11 -94E-14

NOTE: GCONV convergence criterion satisfied.

Fit Statistics
-2 Log Likelihood 1428.2
AIC (smaller is better) 1446.2
AICC (smaller is better) 1446.6
BIC (smaller is better) 1470.1

Parameter Estimates
Parameter Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper Gradient
fint 1.7006 0.2689 104 6.32 <.0001 0.05 1.1674 2.2339 2.4E-12
fsex -0.5174 0.2928 104 -1.77 0.0801 0.05 -1.0980 0.06317 1.44E-12
fage 0.09091 0.04669 104 1.95 0.0542 0.05 -0.00167 0.1835 4.57E-11
fsexage -0.1032 0.05082 104 -2.03 0.0448 0.05 -0.2040 -0.00243 4.89E-11
varEint -0.4871 0.07035 104 -6.92 <.0001 0.05 -0.6266 -0.3476 6.07E-11
vU0int 0.4809 0.2958 104 1.63 0.1070 0.05 -0.1056 1.0674 -315E-15
vU0sex -0.5938 0.3485 104 -1.70 0.0914 0.05 -1.2848 0.09726 2.89E-13
vU0age 0.06828 0.06007 104 1.14 0.2583 0.05 -0.05084 0.1874 3.77E-11
vU0sexage -0.07656 0.06943 104 -1.10 0.2727 0.05 -0.2143 0.06113 3.49E-11

Contrasts
Label Num DF Den DF F Value Pr > F
Multivariate Test of Predictors of Random Intercept Variance 3 104 1.28 0.2843

Additional Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Age Slope for Men 0.09091 0.04669 104 1.95 0.0542 0.05 -0.00167 0.1835
Age Slope for Women -0.01230 0.02007 104 -0.61 0.5415 0.05 -0.05210 0.02751
Level-1 Residual Variance 0.6144 0.04322 104 14.21 <.0001 0.05 0.5287 0.7001
Level-2 Random Intercept Variance Intercept 1.6175 0.4784 104 3.38 0.0010 0.05 0.6688 2.5662
Woman Effect on Random Intercept Variance for Age=80 -0.5938 0.3485 104 -1.70 0.0914 0.05 -1.2848 0.09726
Age Effect on Random Intercept Variance for Men 0.06828 0.06007 104 1.14 0.2583 0.05 -0.05084 0.1874
Age Effect on Random Intercept Variance for Women -0.00828 0.03483 104 -0.24 0.8125 0.05 -0.07735 0.06078
Woman*Age Interaction on Random Intercept Variance -0.07656 0.06943 104 -1.10 0.2727 0.05 -0.2143 0.06113


Eq 7.6: Add Random Scale Factor - blows up

The NLMIXED Procedure

Specifications
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
Distribution for Dependent Variable Normal
Random Effects U0i Scalei
Distribution for Random Effects Normal
Subject Variable PersonID
Optimization Technique Newton-Raphson
Integration Method Adaptive Gaussian Quadrature

Dimensions
Observations Used 509
Observations Not Used 0
Total Observations 509
Subjects 105
Max Obs per Subject 5
Parameters 8
Quadrature Points 1

Parameters
fint fsex fage fsexage varEint vU0int cU0scale vscale NegLogLike
1.7 -0.53 0.1 -0.11 -0.48 0.06 0.006 0.002 715.332877

Iteration History
Iter   Calls NegLogLike Diff MaxGrad Slope
1   24 709.832324 5.500553 149.5791 -58.0413
2 * 36 695.509746 14.32258 127.1861 -44.7832
3 * 48 676.236668 19.27308 83.72875 -29.2572
4   60 659.971209 16.26546 49.64423 -23.4718
5   72 637.577264 22.39394 39.47944 -29.4315
6   370 599.234544 38.34272 47.75268 -376.724
7 * 625 449.194383 150.0402 62.40117 -34262.9

WARNING: Problem needs more than 50 iterations or 500 function calls.

Parameter Estimates
Parameter Estimate Gradient
fint 1.4431 7.420948
fsex -0.2240 5.675623
fage 0.1563 32.71075
fsexage -0.1627 3.345728
varEint -4.2802 -8.42978
vU0int -0.6373 -26.5236
cU0scale 0.7710 62.40117
vscale 6.2531 -49.1348


Eq 7.7: Adding Sex and Age to Predict Heterogeneity of Level-1 Variance

The NLMIXED Procedure

Specifications
Data Set WORK.CHAPTER7A
Dependent Variable symptoms
Distribution for Dependent Variable Normal
Random Effects U0i
Distribution for Random Effects Normal
Subject Variable PersonID
Optimization Technique Newton-Raphson
Integration Method Adaptive Gaussian Quadrature

Dimensions
Observations Used 509
Observations Not Used 0
Total Observations 509
Subjects 105
Max Obs per Subject 5
Parameters 9
Quadrature Points 1

Parameters
fint fsex fage fsexage varEint varEsex varEage varEsexage vU0int NegLogLike
1.7 -0.53 0.1 -0.11 1 0 0 0 1 879.649573

Iteration History
Iter   Calls NegLogLike Diff MaxGrad Slope
1   26 753.539687 126.1099 96.94302 -581.956
2   39 722.840538 30.69915 55.79913 -93.862
3   52 716.013194 6.827344 5.493149 -12.8169
4   65 715.938567 0.074627 0.070044 -0.14801
5   78 715.938555 0.000012 0.000012 -0.00002
6   90 715.938555 5.68E-13 5.82E-10 -686E-15

NOTE: GCONV convergence criterion satisfied.

Fit Statistics
-2 Log Likelihood 1431.9
AIC (smaller is better) 1449.9
AICC (smaller is better) 1450.2
BIC (smaller is better) 1473.8

Parameter Estimates
Parameter Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper Gradient
fint 1.7101 0.2114 104 8.09 <.0001 0.05 1.2908 2.1293 -143E-14
fsex -0.5281 0.2454 104 -2.15 0.0338 0.05 -1.0148 -0.04132 -709E-15
fage 0.09906 0.03724 104 2.66 0.0091 0.05 0.02521 0.1729 -203E-13
fsexage -0.1093 0.04234 104 -2.58 0.0112 0.05 -0.1933 -0.02532 -185E-13
varEint -0.4212 0.1459 104 -2.89 0.0047 0.05 -0.7106 -0.1318 4.62E-11
varEsex -0.08544 0.1669 104 -0.51 0.6098 0.05 -0.4165 0.2456 2.76E-11
varEage 0.01200 0.02384 104 0.50 0.6156 0.05 -0.03527 0.05927 5.82E-10
varEsexage -0.00449 0.02780 104 -0.16 0.8718 0.05 -0.05962 0.05063 4.03E-10
vU0int 0.06478 0.1562 104 0.41 0.6791 0.05 -0.2449 0.3744 -12E-13

Contrasts
Label Num DF Den DF F Value Pr > F
Multivariate Test of Predictors of Residual Variance 3 104 0.23 0.8763

Additional Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
Age Slope for Men 0.09906 0.03724 104 2.66 0.0091 0.05 0.02521 0.1729
Age Slope for Women -0.01023 0.02015 104 -0.51 0.6128 0.05 -0.05018 0.02973
Level-2 Random Intercept Variance 1.0669 0.1666 104 6.40 <.0001 0.05 0.7365 1.3973
Level-1 Residual Variance Intercept 0.6563 0.09578 104 6.85 <.0001 0.05 0.4663 0.8462
Woman Effect on Residual Variance for Age=80 -0.08544 0.1669 104 -0.51 0.6098 0.05 -0.4165 0.2456
Age Effect on Residual Variance for Men 0.01200 0.02384 104 0.50 0.6156 0.05 -0.03527 0.05927
Age Effect on Residual Variance for Women 0.007509 0.01429 104 0.53 0.6005 0.05 -0.02084 0.03585
Woman*Age Interaction on Residual Variance -0.00449 0.02780 104 -0.16 0.8718 0.05 -0.05962 0.05063