Rachel Results

The SAS System

The LOGISTIC Procedure

Model Information
Data Set WORK.RACHEL
Response Variable DAILYA_1
Number of Response Levels 2
Model binary logit
Optimization Technique Fisher's scoring

Number of Observations Read 10548
Number of Observations Used 9973

Response Profile
Ordered
Value
DAILYA_1 Total
Frequency
1 1.00 7680
2 0.00 2293

Probability modeled is DAILYA_1=1.00.



Note: 575 observations were deleted due to missing values for the response or explanatory variables.

Model Convergence Status
Convergence criterion (GCONV=1E-8) satisfied.

Model Fit Statistics
Criterion Intercept Only Intercept and
Covariates
AIC 10756.496 10597.805
SC 10763.704 10641.051
-2 Log L 10754.496 10585.805

Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 168.6917 5 <.0001
Score 172.3725 5 <.0001
Wald 164.9647 5 <.0001

Analysis of Maximum Likelihood Estimates
Parameter DF Estimate Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept 1 1.4688 0.0559 689.7657 <.0001
DAY 1 -0.00615 0.000829 55.1497 <.0001
INT1HAPP 1 0.4371 0.1066 16.8011 <.0001
INT3HAPP 1 -1.8439 0.4729 15.2028 <.0001
DAY*INT1HAPP 1 -0.00088 0.00110 0.6489 0.4205
DAY*INT3HAPP 1 0.0192 0.00352 29.7203 <.0001

Association of Predicted Probabilities and Observed
Responses
Percent Concordant 58.4 Somers' D 0.182
Percent Discordant 40.3 Gamma 0.184
Percent Tied 1.3 Tau-a 0.064
Pairs 17610240 c 0.591



The SAS System

The GLIMMIX Procedure

Model Information
Data Set WORK.RACHEL
Response Variable DAILYA_1
Response Distribution Binary
Link Function Logit
Variance Function Default
Variance Matrix Blocked By ID
Estimation Technique Maximum Likelihood
Likelihood Approximation Gauss-Hermite Quadrature
Degrees of Freedom Method Containment

Number of Observations Read 10548
Number of Observations Used 9973

Response Profile
Ordered
Value
DAILYA_1 Total
Frequency
1 1.00 7680
2 0.00 2293
The GLIMMIX procedure is modeling
the probability that DAILYA_1='1.00'.

Dimensions
G-side Cov. Parameters 1
Columns in X 6
Columns in Z per Subject 1
Subjects (Blocks in V) 69
Max Obs per Subject 274

Optimization Information
Optimization Technique Dual Quasi-Newton
Parameters in Optimization 7
Lower Boundaries 1
Upper Boundaries 0
Fixed Effects Not Profiled
Starting From GLM estimates
Quadrature Points 21

Iteration History
Iteration Restarts Evaluations Objective
Function
Change Max
Gradient
0 0 4 7536.9715956 . 10748.05
1 0 8 7519.4216762 17.54991937 8647.604
2 0 8 7509.7206012 9.70107503 778.7812
3 0 5 7509.4780918 0.24250943 397.1777
4 0 4 7475.1593533 34.31873844 108.1279
5 0 2 7465.1506232 10.00873016 479.5172
6 0 2 7458.7979599 6.35266325 931.3601
7 0 2 7453.6239161 5.17404380 1943.518
8 0 2 7452.2506 1.37331614 2368.89
9 0 4 7448.7196532 3.53094678 404.9993
10 0 3 7448.1135136 0.60613955 269.4793
11 0 3 7448.0285815 0.08493216 354.1785
12 0 4 7447.6473296 0.38125183 287.4741
13 0 3 7447.6042591 0.04307052 6.635375
14 0 3 7447.6035034 0.00075571 1.682988
15 0 3 7447.6034881 0.00001527 0.095348

Convergence criterion (GCONV=1E-8) satisfied.

Fit Statistics
-2 Log Likelihood 7447.60
AIC (smaller is better) 7461.60
AICC (smaller is better) 7461.61
BIC (smaller is better) 7477.24
CAIC (smaller is better) 7484.24
HQIC (smaller is better) 7467.81

Fit Statistics for Conditional Distribution
-2 log L(DAILYA_1 | r. effects) 7125.78
Pearson Chi-Square 8988.67
Pearson Chi-Square / DF 0.90

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error
Intercept ID 3.7895 0.7214

Solutions for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t|
Intercept 2.2881 0.2498 68 9.16 <.0001
DAY -0.00819 0.001322 9899 -6.20 <.0001
INT1HAPP -0.2560 0.1468 9899 -1.74 0.0813
INT3HAPP 0.5713 0.6022 9899 0.95 0.3428
DAY*INT1HAPP 0.005158 0.001389 9899 3.71 0.0002
DAY*INT3HAPP -0.00355 0.004362 9899 -0.81 0.4162

Type III Tests of Fixed Effects
Effect Num DF Den DF F Value Pr > F
DAY 1 9899 38.39 <.0001
INT1HAPP 1 9899 3.04 0.0813
INT3HAPP 1 9899 0.90 0.3428
DAY*INT1HAPP 1 9899 13.79 0.0002
DAY*INT3HAPP 1 9899 0.66 0.4162



The SAS System

The GLIMMIX Procedure

Model Information
Data Set WORK.RACHEL
Response Variable DAILYA_1
Response Distribution Binary
Link Function Logit
Variance Function Default
Variance Matrix Blocked By ID
Estimation Technique Maximum Likelihood
Likelihood Approximation Gauss-Hermite Quadrature
Degrees of Freedom Method Containment

Number of Observations Read 10548
Number of Observations Used 9973

Response Profile
Ordered
Value
DAILYA_1 Total
Frequency
1 1.00 7680
2 0.00 2293
The GLIMMIX procedure is modeling
the probability that DAILYA_1='1.00'.

Dimensions
G-side Cov. Parameters 3
Columns in X 6
Columns in Z per Subject 2
Subjects (Blocks in V) 69
Max Obs per Subject 274

Optimization Information
Optimization Technique Dual Quasi-Newton
Parameters in Optimization 9
Lower Boundaries 2
Upper Boundaries 0
Fixed Effects Not Profiled
Starting From GLM estimates
Quadrature Points 11

Iteration History
Iteration Restarts Evaluations Objective
Function
Change Max
Gradient
0 0 4 7486.7535634 . 7287939
1 0 24 7405.3214378 81.43212553 159160.2
2 0 3 7395.3810963 9.94034150 125306.7
3 0 3 7391.5583414 3.82275495 126260.5
4 0 3 7390.4317331 1.12660824 149368.3
5 0 5 7390.4163014 0.01543169 150686.2
6 0 6 7370.0378828 20.37841869 86988.87
7 0 3 7366.255753 3.78212975 90282.82
8 0 2 7359.7772659 6.47848709 43699.27
9 0 2 7352.5045813 7.27268459 6071.317
10 0 3 7351.6867967 0.81778462 7469.696
11 0 3 7351.6418677 0.04492904 7902.039
12 0 4 7351.5513723 0.09049542 12088.92
13 0 2 7351.4425113 0.10886095 10843.53
14 0 3 7351.3881168 0.05439446 8537.032
15 0 3 7351.3630354 0.02508149 3541.876
16 0 3 7351.3613962 0.00163918 3965.505
17 0 3 7351.360851 0.00054519 3707.766
18 0 4 7351.3584753 0.00237574 606.5266
19 0 3 7351.3579696 0.00050567 1.954222
20 0 3 7351.3579683 0.00000126 0.109197

Convergence criterion (GCONV=1E-8) satisfied.

Fit Statistics
-2 Log Likelihood 7351.36
AIC (smaller is better) 7369.36
AICC (smaller is better) 7369.38
BIC (smaller is better) 7389.46
CAIC (smaller is better) 7398.46
HQIC (smaller is better) 7377.34

Fit Statistics for Conditional Distribution
-2 log L(DAILYA_1 | r. effects) 6915.95
Pearson Chi-Square 8550.20
Pearson Chi-Square / DF 0.86

Estimated G Correlation Matrix
Effect Row Col1 Col2
Intercept 1 1.0000 -0.08167
DAY 2 -0.08167 1.0000

Covariance Parameter Estimates
Cov Parm Subject Estimate Standard Error
UN(1,1) ID 3.8758 0.8423
UN(2,1) ID -0.00150 0.003654
UN(2,2) ID 0.000087 .

Solutions for Fixed Effects
Effect Estimate Standard Error DF t Value Pr > |t|
Intercept 2.3893 0.2614 68 9.14 <.0001
DAY -0.01056 0.001994 68 -5.30 <.0001
INT1HAPP 0.1598 0.1724 9831 0.93 0.3539
INT3HAPP 1.1592 0.7379 9831 1.57 0.1162
DAY*INT1HAPP 0.001781 0.001637 9831 1.09 0.2764
DAY*INT3HAPP -0.00627 0.005484 9831 -1.14 0.2526

Type III Tests of Fixed Effects
Effect Num DF Den DF F Value Pr > F
DAY 1 68 28.07 <.0001
INT1HAPP 1 9831 0.86 0.3539
INT3HAPP 1 9831 2.47 0.1162
DAY*INT1HAPP 1 9831 1.18 0.2764
DAY*INT3HAPP 1 9831 1.31 0.2526

Solution for Random Effects
Effect Subject Estimate Std Err Pred DF t Value Pr > |t|
Intercept ID 3001 0.4895 0.5149 9831 0.95 0.3418
DAY ID 3001 -0.01021 0.004460 9831 -2.29 0.0221
Intercept ID 3002 -0.2505 0.5374 9831 -0.47 0.6411
DAY ID 3002 0.01046 0.005866 9831 1.78 0.0747
Intercept ID 3003 -1.2245 0.4184 9831 -2.93 0.0034
DAY ID 3003 -0.00225 0.003914 9831 -0.57 0.5661
Intercept ID 3004 -2.6147 0.4342 9831 -6.02 <.0001
DAY ID 3004 -0.00007 0.005954 9831 -0.01 0.9903
Intercept ID 3005 -2.0225 0.4182 9831 -4.84 <.0001
DAY ID 3005 0.006873 0.004692 9831 1.46 0.1430
Intercept ID 3006 -1.4759 0.4263 9831 -3.46 0.0005
DAY ID 3006 0.006991 0.004448 9831 1.57 0.1161
Intercept ID 3007 1.4723 0.7133 9831 2.06 0.0390
DAY ID 3007 -0.00445 0.006581 9831 -0.68 0.4992
Intercept ID 3008 -1.1454 0.4338 9831 -2.64 0.0083
DAY ID 3008 -0.00018 0.004509 9831 -0.04 0.9677
Intercept ID 3009 -2.8485 0.4288 9831 -6.64 <.0001
DAY ID 3009 0.000830 0.004377 9831 0.19 0.8497
Intercept ID 3010 2.5548 0.9732 9831 2.63 0.0087
DAY ID 3010 -0.00140 0.008414 9831 -0.17 0.8682
Intercept ID 3011 -0.3810 0.5020 9831 -0.76 0.4479
DAY ID 3011 -0.00484 0.007906 9831 -0.61 0.5406
Intercept ID 3012 0.9965 0.6500 9831 1.53 0.1253
DAY ID 3012 0.001934 0.005574 9831 0.35 0.7286
Intercept ID 3013 0.8272 0.6127 9831 1.35 0.1770
DAY ID 3013 0.002206 0.005121 9831 0.43 0.6667
Intercept ID 3014 -0.6295 0.4880 9831 -1.29 0.1971
DAY ID 3014 0.006999 0.005390 9831 1.30 0.1941
Intercept ID 3015 -0.7841 0.4415 9831 -1.78 0.0758
DAY ID 3015 -0.01242 0.004890 9831 -2.54 0.0111
Intercept ID 3016 -1.6655 0.4074 9831 -4.09 <.0001
DAY ID 3016 0.001635 0.003829 9831 0.43 0.6694
Intercept ID 3017 -4.8193 0.6564 9831 -7.34 <.0001
DAY ID 3017 -0.00965 0.008419 9831 -1.15 0.2518
Intercept ID 3018 1.8550 0.7289 9831 2.54 0.0109
DAY ID 3018 -0.00659 0.005181 9831 -1.27 0.2036
Intercept ID 3019 3.2676 1.1773 9831 2.78 0.0055
DAY ID 3019 0.005923 0.008573 9831 0.69 0.4896
Intercept ID 3020 -0.4369 0.4953 9831 -0.88 0.3778
DAY ID 3020 0.003408 0.005453 9831 0.62 0.5320
Intercept ID 3021 2.5770 0.9654 9831 2.67 0.0076
DAY ID 3021 -0.00093 0.008503 9831 -0.11 0.9129
Intercept ID 3022 -0.4056 0.4884 9831 -0.83 0.4063
DAY ID 3022 -0.00015 0.005145 9831 -0.03 0.9764
Intercept ID 3023 1.2110 0.6488 9831 1.87 0.0620
DAY ID 3023 -0.01004 0.006144 9831 -1.63 0.1022
Intercept ID 3024 1.0567 0.6501 9831 1.63 0.1041
DAY ID 3024 -0.00586 0.008177 9831 -0.72 0.4739
Intercept ID 3025 -0.1328 0.5512 9831 -0.24 0.8096
DAY ID 3025 -0.00182 0.006158 9831 -0.30 0.7673
Intercept ID 3026 0.5889 0.5577 9831 1.06 0.2910
DAY ID 3026 -0.01461 0.006784 9831 -2.15 0.0313
Intercept ID 3027 -0.6532 0.4631 9831 -1.41 0.1584
DAY ID 3027 -0.00050 0.005043 9831 -0.10 0.9209
Intercept ID 3028 -1.3151 0.4512 9831 -2.91 0.0036
DAY ID 3028 0.01075 0.005025 9831 2.14 0.0325
Intercept ID 3029 1.8916 0.7910 9831 2.39 0.0168
DAY ID 3029 0.000012 0.006031 9831 0.00 0.9984
Intercept ID 3030 -0.4390 0.4927 9831 -0.89 0.3730
DAY ID 3030 0.003500 0.004994 9831 0.70 0.4834
Intercept ID 3032 -3.4620 0.4969 9831 -6.97 <.0001
DAY ID 3032 -0.01865 0.007155 9831 -2.61 0.0092
Intercept ID 3033 1.1891 0.7043 9831 1.69 0.0914
DAY ID 3033 -0.00081 0.006928 9831 -0.12 0.9073
Intercept ID 3035 -1.7791 0.4059 9831 -4.38 <.0001
DAY ID 3035 0.000977 0.004018 9831 0.24 0.8080
Intercept ID 3036 3.0951 1.1358 9831 2.73 0.0064
DAY ID 3036 0.004118 0.008999 9831 0.46 0.6472
Intercept ID 3037 1.0846 0.6439 9831 1.68 0.0921
DAY ID 3037 -0.00720 0.006862 9831 -1.05 0.2940
Intercept ID 3038 1.4823 0.7540 9831 1.97 0.0493
DAY ID 3038 0.003086 0.006216 9831 0.50 0.6195
Intercept ID 3039 1.0367 0.6559 9831 1.58 0.1140
DAY ID 3039 0.003823 0.005363 9831 0.71 0.4760
Intercept ID 3040 -0.6120 0.4765 9831 -1.28 0.1990
DAY ID 3040 -0.00100 0.005296 9831 -0.19 0.8499
Intercept ID 3041 -1.6073 0.4302 9831 -3.74 0.0002
DAY ID 3041 -0.00876 0.005059 9831 -1.73 0.0834
Intercept ID 3042 1.6571 0.8532 9831 1.94 0.0521
DAY ID 3042 0.01270 0.007166 9831 1.77 0.0763
Intercept ID 3043 1.6449 0.8002 9831 2.06 0.0399
DAY ID 3043 0.006694 0.007316 9831 0.92 0.3602
Intercept ID 3044 3.2007 1.1601 9831 2.76 0.0058
DAY ID 3044 0.005193 0.008737 9831 0.59 0.5523
Intercept ID 3045 -2.7915 0.4093 9831 -6.82 <.0001
DAY ID 3045 -0.00003 0.003932 9831 -0.01 0.9944
Intercept ID 4001 -1.1080 0.4146 9831 -2.67 0.0075
DAY ID 4001 0.000075 0.003382 9831 0.02 0.9824
Intercept ID 4002 -0.9607 0.4397 9831 -2.18 0.0289
DAY ID 4002 -0.00154 0.004578 9831 -0.34 0.7361
Intercept ID 4003 -0.8197 0.4855 9831 -1.69 0.0914
DAY ID 4003 0.009727 0.006361 9831 1.53 0.1263
Intercept ID 4004 2.5212 0.9456 9831 2.67 0.0077
DAY ID 4004 -0.00263 0.007512 9831 -0.35 0.7266
Intercept ID 4005 1.9402 0.8472 9831 2.29 0.0220
DAY ID 4005 0.001172 0.007160 9831 0.16 0.8699
Intercept ID 4006 -3.2568 0.4663 9831 -6.98 <.0001
DAY ID 4006 0.001043 0.008746 9831 0.12 0.9050
Intercept ID 4008 0.9978 0.6241 9831 1.60 0.1099
DAY ID 4008 -0.00563 0.005848 9831 -0.96 0.3359
Intercept ID 4010 1.3456 0.6720 9831 2.00 0.0453
DAY ID 4010 -0.00272 0.005478 9831 -0.50 0.6191
Intercept ID 4011 -0.4703 0.4766 9831 -0.99 0.3238
DAY ID 4011 0.01047 0.004069 9831 2.57 0.0101
Intercept ID 4012 0.2079 0.6421 9831 0.32 0.7462
DAY ID 4012 -0.00087 0.009201 9831 -0.09 0.9248
Intercept ID 4013 -0.7058 0.4004 9831 -1.76 0.0780
DAY ID 4013 -0.00426 0.002869 9831 -1.49 0.1373
Intercept ID 4014 -2.9540 0.4595 9831 -6.43 <.0001
DAY ID 4014 -0.02070 0.006526 9831 -3.17 0.0015
Intercept ID 4015 -3.6762 0.4229 9831 -8.69 <.0001
DAY ID 4015 0.01166 0.003295 9831 3.54 0.0004
Intercept ID 4017 -0.9364 0.4818 9831 -1.94 0.0520
DAY ID 4017 0.002103 0.004400 9831 0.48 0.6327
Intercept ID 4018 1.0360 0.5890 9831 1.76 0.0786
DAY ID 4018 -0.01158 0.005286 9831 -2.19 0.0285
Intercept ID 4019 0.7604 0.6600 9831 1.15 0.2493
DAY ID 4019 0.006610 0.006392 9831 1.03 0.3011
Intercept ID 4020 -3.0080 0.3884 9831 -7.74 <.0001
DAY ID 4020 0.01183 0.002320 9831 5.10 <.0001
Intercept ID 4021 -0.3540 0.5193 9831 -0.68 0.4955
DAY ID 4021 0.01106 0.003987 9831 2.78 0.0055
Intercept ID 4022 -0.7663 0.4833 9831 -1.59 0.1129
DAY ID 4022 0.01061 0.004122 9831 2.57 0.0101
Intercept ID 4024 -0.6108 0.4508 9831 -1.35 0.1755
DAY ID 4024 0.003796 0.003748 9831 1.01 0.3112
Intercept ID 4025 0.7299 0.6048 9831 1.21 0.2275
DAY ID 4025 -0.00040 0.005425 9831 -0.07 0.9416
Intercept ID 4026 0.5150 0.5521 9831 0.93 0.3510
DAY ID 4026 -0.00201 0.004569 9831 -0.44 0.6594
Intercept ID 4027 2.3348 0.9330 9831 2.50 0.0124
DAY ID 4027 0.000289 0.008616 9831 0.03 0.9732
Intercept ID 4029 -0.5361 0.4875 9831 -1.10 0.2715
DAY ID 4029 -0.00182 0.006918 9831 -0.26 0.7923
Intercept ID 4030 2.3637 0.8553 9831 2.76 0.0057
DAY ID 4030 -0.00484 0.007214 9831 -0.67 0.5019
Intercept ID 4031 0.9105 0.6662 9831 1.37 0.1718
DAY ID 4031 0.004637 0.006025 9831 0.77 0.4415



The SAS System

The NLMIXED Procedure

Specifications
Data Set WORK.RACHEL
Dependent Variable DAILYA_1
Distribution for Dependent Variable General
Random Effects u
Distribution for Random Effects Normal
Subject Variable ID
Optimization Technique Dual Quasi-Newton
Integration Method Adaptive Gaussian Quadrature

Dimensions
Observations Used 9973
Observations Not Used 575
Total Observations 10548
Subjects 69
Max Obs Per Subject 274
Parameters 7
Quadrature Points 21

Parameters
b0 bday bINT1 bINT3 bdayInt1 bdayInt3 varu NegLogLike
1.4688 -0.00615 0.4371 -1.8439 -0.00088 0.0192 1 3794.55121

Iteration History
Iter   Calls NegLogLike Diff MaxGrad Slope
1   5 3786.69144 7.859766 5263.547 -273955
2   10 3784.45338 2.238061 2464.7 -231818
3   14 3784.12912 0.324263 2286.989 -2952.61
4   16 3749.9894 34.13972 629.0194 -58.6274
5   18 3734.31493 15.67447 161.6733 -14.4769
6   19 3730.06642 4.248508 2476.618 -1.83904
7   21 3727.7659 2.300525 960.613 -3.01966
8   22 3725.91837 1.84753 800.5814 -0.79258
9   24 3724.76924 1.149124 204.0517 -1.54379
10   26 3724.43147 0.337772 166.4823 -0.33067
11   28 3724.35708 0.074391 294.134 -0.04244
12   30 3723.88761 0.469469 416.3803 -0.08245
13   32 3723.80375 0.083857 37.24948 -0.14062
14   34 3723.80179 0.001964 11.65407 -0.00382
15   36 3723.80175 0.000042 1.42528 -0.00009
16   38 3723.80174 2.1E-6 0.123566 -4.5E-6

NOTE: GCONV convergence criterion satisfied.

Fit Statistics
-2 Log Likelihood 7447.6
AIC (smaller is better) 7461.6
AICC (smaller is better) 7461.6
BIC (smaller is better) 7477.2

Parameter Estimates
Parameter Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper Gradient
b0 2.2881 0.2498 68 9.16 <.0001 0.05 1.7896 2.7866 -0.00032
bday -0.00819 0.001322 68 -6.20 <.0001 0.05 -0.01083 -0.00555 0.063351
bINT1 -0.2560 0.1468 68 -1.74 0.0857 0.05 -0.5490 0.03697 0.000728
bINT3 0.5714 0.6021 68 0.95 0.3460 0.05 -0.6302 1.7729 0.000343
bdayInt1 0.005158 0.001389 68 3.71 0.0004 0.05 0.002386 0.007929 0.123566
bdayInt3 -0.00355 0.004362 68 -0.81 0.4188 0.05 -0.01225 0.005156 0.023915
varu 3.7895 0.7214 68 5.25 <.0001 0.05 2.3500 5.2289 0.000128

Additional Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
icc 0.5353 0.04735 68 11.30 <.0001 0.05 0.4408 0.6298



The SAS System

The NLMIXED Procedure

Specifications
Data Set WORK.RACHEL
Dependent Variable DAILYA_1
Distribution for Dependent Variable General
Random Effects u0 u1
Distribution for Random Effects Normal
Subject Variable ID
Optimization Technique Dual Quasi-Newton
Integration Method Adaptive Gaussian Quadrature

Dimensions
Observations Used 9973
Observations Not Used 575
Total Observations 10548
Subjects 69
Max Obs Per Subject 274
Parameters 9
Quadrature Points 21

Parameters
b0 bday bINT1 bINT3 bdayInt1 bdayInt3 v0 c01 v1 NegLogLike
1.4688 -0.00615 0.4371 -1.8439 -0.00088 0.0192 3.7895 0 1 3933.87504

Iteration History
Iter   Calls NegLogLike Diff MaxGrad Slope
1   5 3930.48794 3.387102 326.0222 -31343.1
2   9 3930.40261 0.085335 32.50515 -1140.1
3   28 3795.76035 134.6423 749.0345 -12.8584
4   34 3795.75917 0.001175 757.9611 -934.438
5   40 3771.706 24.05318 22362.09 -110.54
6   42 3768.04812 3.657878 36897.66 -138.208
7   44 3758.40472 9.643398 103869.7 -1429.22
8   48 3746.63946 11.76526 218107 -1361.13
9   49 3726.38121 20.25826 627197.3 -227.274
10   51 3711.21513 15.16608 617736.2 -282.9
11   52 3694.40731 16.80782 447924.8 -310.874
12   53 3688.73355 5.673762 9682.882 -95.2552
13   54 3685.25035 3.483201 39600.91 -2.86621
14   55 3682.35789 2.892461 14061.52 -7.09375
15   58 3680.77468 1.583204 6540.578 -1.66274
16   61 3680.34006 0.434622 33753.83 -0.82327
17   62 3679.63442 0.705644 2615.321 -0.79034
18   63 3678.45462 1.179799 18454.7 -0.92178
19   64 3677.73769 0.716923 33371.13 -0.91841
20   65 3676.98542 0.752276 20062.14 -0.86944
21   66 3676.1407 0.844721 18507.43 -0.5223
22   68 3675.81421 0.32649 7092.596 -0.48515
23   70 3675.75362 0.060587 2998.068 -0.07926
24   72 3675.71725 0.036371 4508.433 -0.04012
25   74 3675.69865 0.018601 3270.645 -0.01568
26   76 3675.68996 0.008686 809.4154 -0.0107
27   78 3675.68857 0.001397 451.7549 -0.00205
28   79 3675.68804 0.000525 594.4297 -0.00069
29   80 3675.68717 0.000873 204.5019 -0.00146
30   81 3675.6862 0.000967 444.9492 -0.00067
31   83 3675.6802 0.006001 578.8427 -0.00121
32   85 3675.67885 0.001351 15.4652 -0.00216
33   87 3675.67884 6.593E-6 4.916101 -0.00001

NOTE: GCONV convergence criterion satisfied.

Fit Statistics
-2 Log Likelihood 7351.4
AIC (smaller is better) 7369.4
AICC (smaller is better) 7369.4
BIC (smaller is better) 7389.5

Parameter Estimates
Parameter Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper Gradient
b0 2.3893 0.2614 67 9.14 <.0001 0.05 1.8675 2.9110 -0.00064
bday -0.01056 0.001994 67 -5.30 <.0001 0.05 -0.01454 -0.00658 -0.12359
bINT1 0.1598 0.1724 67 0.93 0.3573 0.05 -0.1843 0.5039 -0.00194
bINT3 1.1592 0.7378 67 1.57 0.1208 0.05 -0.3134 2.6318 -0.0003
bdayInt1 0.001781 0.001637 67 1.09 0.2803 0.05 -0.00149 0.005048 -0.17425
bdayInt3 -0.00627 0.005483 67 -1.14 0.2565 0.05 -0.01722 0.004669 -0.04241
v0 3.8759 0.8423 67 4.60 <.0001 0.05 2.1945 5.5572 0.000016
c01 -0.00150 0.003654 67 -0.41 0.6830 0.05 -0.00879 0.005795 -0.01358
v1 0.000087 0.000027 67 3.19 0.0021 0.05 0.000033 0.000141 -4.9161

Additional Estimates
Label Estimate Standard Error DF t Value Pr > |t| Alpha Lower Upper
re corr -0.08169 0.1950 67 -0.42 0.6766 0.05 -0.4709 0.3076

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