use alcohol2, clear
/* Table 8.1 pg 282 */
*table of sample observations with exponentiated variables
preserve
replace alc1 = exp(alc1)
replace alc2 = exp(alc2)
replace alc3 = exp(alc3)
replace peer1 = exp(peer1)
replace peer2 = exp(peer2)
replace peer3 = exp(peer3)
format alc1-alc3 %4.2f
list if inlist(id, 18, 21, 236, 335, 353, 555, 850, 883, 974, 1012)
+------------------------------------------------------------+
| id female alc1 alc2 alc3 peer1 peer2 peer3 |
|------------------------------------------------------------|
18. | 18 0 1.00 1.33 2.00 3 2 2 |
21. | 21 0 2.00 1.00 1.33 1 5 5 |
236. | 236 0 3.33 4.33 4.33 2 1 4 |
335. | 335 0 1.00 1.33 1.67 1 2 1 |
353. | 353 0 2.00 2.00 1.67 1 1 2 |
|------------------------------------------------------------|
555. | 555 1 2.67 2.33 1.67 2 3 1 |
850. | 850 1 1.33 1.67 1.33 3 1 2 |
883. | 883 1 3.00 2.67 3.33 4 5 1 |
974. | 974 1 1.00 1.67 2.67 1 5 6 |
1012. | 1012 1 1.00 1.67 2.33 1 2 4 |
+------------------------------------------------------------+
*get back to log values
replace alc1 = log(alc1)
replace alc2 = log(alc2)
replace alc3 = log(alc3)
replace peer1 =log(peer1)
replace peer2 = log(peer2)
replace peer3 = log(peer3)
*tables of means and covariances
tabstat female alc1-alc3 peer1-peer3, columns(statistics)
variable | mean
-------------+----------
female | .6122995
alc1 | .2250666
alc2 | .2541351
alc3 | .287923
peer1 | .1771944
peer2 | .2904569
peer3 | .3470381
------------------------
corr female alc1-alc3 peer1-peer3, cov
| female alc1 alc2 alc3 peer1 peer2 peer3
-------------+---------------------------------------------------------------
female | .237601
alc1 | -.008431 .135587
alc2 | -.012693 .077753 .155281
alc3 | -.004875 .065265 .081864 .180759
peer1 | -.008513 .06587 .044797 .039882 .173992
peer2 | -.022059 .064049 .096479 .06581 .071582 .261902
peer3 | -.023753 .060082 .074331 .13197 .070713 .111806 .289012
/* Table 8.2 Selected Latent Growth Models pg 289 */
*model A
sem (alc1 <- Intercept@1 Slope@0 _cons@0) ///
(alc2 <- Intercept@1 Slope@0.75 _cons@0) ///
(alc3 <- Intercept@1 Slope@1.75 _cons@0), ///
var(e.alc1 e.alc2 e.alc3) ///
means(Intercept Slope)
Endogenous variables
Measurement: alc1 alc2 alc3
Exogenous variables
Latent: Intercept Slope
Fitting target model:
Iteration 0: log likelihood = -1296.0147
Iteration 1: log likelihood = -1293.4055
Iteration 2: log likelihood = -1280.8849
Iteration 3: log likelihood = -1280.8537
Iteration 4: log likelihood = -1280.8537
Structural equation model Number of obs = 1122
Estimation method = ml
Log likelihood = -1280.8537
( 1) [alc1]Intercept = 1
( 2) [alc2]Intercept = 1
( 3) [alc2]Slope = .75
( 4) [alc3]Intercept = 1
( 5) [alc3]Slope = 1.75
( 6) [alc1]_cons = 0
( 7) [alc2]_cons = 0
( 8) [alc3]_cons = 0
------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Measurement |
alc1 <- |
Intercept | 1 (constrained)
_cons | 0 (constrained)
-----------+----------------------------------------------------------------
alc2 <- |
Intercept | 1 (constrained)
Slope | .75 (constrained)
_cons | 0 (constrained)
-----------+----------------------------------------------------------------
alc3 <- |
Intercept | 1 (constrained)
Slope | 1.75 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
Mean |
Intercept | .2256248 .0106901 21.11 0.000 .2046726 .2465771
Slope | .0359778 .0073456 4.90 0.000 .0215808 .0503749
-------------+----------------------------------------------------------------
Variance |
e.alc1 | .0484275 .006414 .0373555 .0627812
e.alc2 | .0757024 .0044396 .0674823 .0849237
e.alc3 | .0766982 .009889 .0595713 .0987492
Intercept | .0870391 .0071037 .0741727 .1021374
Slope | .0197635 .0052081 .0117911 .0331265
-------------+----------------------------------------------------------------
Covariance |
Intercept |
Slope | -.0124755 .0045745 -2.73 0.006 -.0214413 -.0035098
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(1) = 0.05, Prob > chi2 = 0.8262
*model B
sem (alc1 <- Intercept@1 Slope@0 _cons@0) ///
(alc2 <- Intercept@1 Slope@0.75 _cons@0) ///
(alc3 <- Intercept@1 Slope@1.75 _cons@0) ///
(Intercept <- Female _cons) ///
(Slope <- Female _cons) ///
(female <- Female@1 _cons@0), ///
var(e.alc1 e.alc2 e.alc3 e.female@0) ///
cov(e.Intercept*e.Slope)
Endogenous variables
Measurement: alc1 alc2 alc3 female
Latent: Intercept Slope
Exogenous variables
Latent: Female
Fitting target model:
Iteration 0: log likelihood = -3330.1757
Iteration 1: log likelihood = -3131.5527
Iteration 2: log likelihood = -2694.4751
Iteration 3: log likelihood = -2602.5849
Iteration 4: log likelihood = -2595.833
Iteration 5: log likelihood = -2595.8024
Iteration 6: log likelihood = -2595.8024
Structural equation model Number of obs = 1122
Estimation method = ml
Log likelihood = -2595.8024
( 1) [alc1]Intercept = 1
( 2) [alc2]Intercept = 1
( 3) [alc2]Slope = .75
( 4) [alc3]Intercept = 1
( 5) [alc3]Slope = 1.75
( 6) [female]Female = 1
( 7) [var(e.female)]_cons = 0
( 8) [alc1]_cons = 0
( 9) [alc2]_cons = 0
(10) [alc3]_cons = 0
(11) [female]_cons = 0
------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Structural |
Inter~t <- |
Female | -.0419189 .0219193 -1.91 0.056 -.0848799 .0010421
_cons | .2512966 .0171494 14.65 0.000 .2176843 .2849089
-----------+----------------------------------------------------------------
Slope <- |
Female | .0078759 .0150842 0.52 0.602 -.0216885 .0374404
_cons | .0311545 .0118017 2.64 0.008 .0080235 .0542855
-------------+----------------------------------------------------------------
Measurement |
alc1 <- |
Intercept | 1 (constrained)
_cons | 0 (constrained)
-----------+----------------------------------------------------------------
alc2 <- |
Intercept | 1 (constrained)
Slope | .75 (constrained)
_cons | 0 (constrained)
-----------+----------------------------------------------------------------
alc3 <- |
Intercept | 1 (constrained)
Slope | 1.75 (constrained)
_cons | 0 (constrained)
-----------+----------------------------------------------------------------
female <- |
Female | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
Variance |
e.alc1 | .0488516 .0064142 .0377674 .0631889
e.alc2 | .0754942 .0044315 .0672897 .0846991
e.alc3 | .0770778 .0098952 .0599311 .0991303
e.female | 0 (constrained)
e.Interc~t | .086326 .0070813 .0735051 .1013832
e.Slope | .0194839 .0052097 .0115366 .0329058
Female | .6122994 .0258513 .5636715 .6651224
-------------+----------------------------------------------------------------
Covariance |
e.Interc~t |
e.Slope | -.0121602 .004569 -2.66 0.008 -.0211153 -.003205
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(3) = 1064.66, Prob > chi2 = 0.0000
*model C
sem (alc1 <- Intercept@1 Slope@0 _cons@0) ///
(alc2 <- Intercept@1 Slope@0.75 _cons@0) ///
(alc3 <- Intercept@1 Slope@1.75 _cons@0) ///
(Intercept <- Female _cons) ///
(Slope <- _cons) ///
(female <- Female@1 _cons@0), ///
var(e.alc1 e.alc2 e.alc3 e.female@0) ///
cov(e.Intercept*e.Slope)
Measurement: alc1 alc2 alc3 female
Latent: Intercept Slope
Exogenous variables
Latent: Female
Fitting target model:
Iteration 0: log likelihood = -3337.9641
Iteration 1: log likelihood = -3176.6645
Iteration 2: log likelihood = -2702.2814
Iteration 3: log likelihood = -2603.6412
Iteration 4: log likelihood = -2595.9756
Iteration 5: log likelihood = -2595.9387
Iteration 6: log likelihood = -2595.9387
Structural equation model Number of obs = 1122
Estimation method = ml
Log likelihood = -2595.9387
( 1) [alc1]Intercept = 1
( 2) [alc2]Intercept = 1
( 3) [alc2]Slope = .75
( 4) [alc3]Intercept = 1
( 5) [alc3]Slope = 1.75
( 6) [female]Female = 1
( 7) [var(e.female)]_cons = 0
( 8) [alc1]_cons = 0
( 9) [alc2]_cons = 0
(10) [alc3]_cons = 0
(11) [female]_cons = 0
------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
Structural |
Inter~t <- |
Female | -.0366032 .0194139 -1.89 0.059 -.0746537 .0014473
_cons | .2480412 .0159772 15.52 0.000 .2167265 .2793559
-----------+----------------------------------------------------------------
Slope <- |
_cons | .0359778 .0073456 4.90 0.000 .0215808 .0503749
-------------+----------------------------------------------------------------
Measurement |
alc1 <- |
Intercept | 1 (constrained)
_cons | 0 (constrained)
-----------+----------------------------------------------------------------
alc2 <- |
Intercept | 1 (constrained)
Slope | .75 (constrained)
_cons | 0 (constrained)
-----------+----------------------------------------------------------------
alc3 <- |
Intercept | 1 (constrained)
Slope | 1.75 (constrained)
_cons | 0 (constrained)
-----------+----------------------------------------------------------------
female <- |
Female | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
Variance |
e.alc1 | .0487978 .0064128 .0377172 .0631337
e.alc2 | .0754908 .0044314 .0672865 .0846955
e.alc3 | .077192 .0098948 .0600428 .0992392
e.female | 0 (constrained)
e.Interc~t | .0863703 .0070832 .0735457 .1014311
e.Slope | .0194814 .0052098 .0115343 .0329042
Female | .6122995 .0258513 .5636716 .6651225
-------------+----------------------------------------------------------------
Covariance |
e.Interc~t |
e.Slope | -.0121897 .0045713 -2.67 0.008 -.0211492 -.0032301
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(4) = 1064.94, Prob > chi2 = 0.0000
/* model D */
sem (alc1 <- Intercept@1 Slope@0 _cons@0) ///
(alc2 <- Intercept@1 Slope@0.75 _cons@0) ///
(alc3 <- Intercept@1 Slope@1.75 _cons@0) ///
(peer1 <- Int_peer@1 Slope_peer@0 _cons@0) ///
(peer2 <- Int_peer@1 Slope_peer@0.75 _cons@0) ///
(peer3 <- Int_peer@1 Slope_peer@1.75 _cons@0) ///
(Intercept <- Int_peer Slope_peer _cons) ///
(Slope <- Int_peer Slope_peer _cons), ///
var(e.alc1 e.alc2 e.alc3 ///
e.peer1 e.peer2 e.peer3 ///
(Int_peer, init(.1)) ///
(Slope_peer, init(.1)) ///
(e.Intercept, init(.1)) ///
(e.Slope, init(.1))) ///
cov(e.alc1*e.peer1 e.alc2*e.peer2 e.alc3*e.peer3 ///
(e.Intercept*e.Slope, init(0)) ///
(Int_peer*Slope_peer, init(0))) ///
means((Int_peer, init(.2)) ///
(Slope_peer, init(.1)))
Endogenous variables
Measurement: alc1 alc2 alc3 peer1 peer2 peer3
Latent: Intercept Slope
Exogenous variables
Latent: Int_peer Slope_peer
Fitting target model:
Iteration 0: log likelihood = -4015.0953 (not concave)
Iteration 1: log likelihood = -3745.7065 (not concave)
Iteration 2: log likelihood = -3397.2708 (not concave)
Iteration 3: log likelihood = -3181.9038 (not concave)
Iteration 4: log likelihood = -3083.7416 (not concave)
Iteration 5: log likelihood = -3059.976 (not concave)
Iteration 6: log likelihood = -3053.5296 (not concave)
Iteration 7: log likelihood = -3047.9787 (not concave)
Iteration 8: log likelihood = -3045.1081 (not concave)
Iteration 9: log likelihood = -3044.4217 (not concave)
Iteration 10: log likelihood = -3043.4394 (not concave)
Iteration 11: log likelihood = -3042.7906 (not concave)
Iteration 12: log likelihood = -3042.594 (not concave)
Iteration 13: log likelihood = -3042.3991 (not concave)
Iteration 14: log likelihood = -3041.6272 (not concave)
Iteration 15: log likelihood = -3041.4459 (not concave)
Iteration 16: log likelihood = -3041.3146 (not concave)
Iteration 17: log likelihood = -3041.1982 (not concave)
Iteration 18: log likelihood = -3041.0967 (not concave)
Iteration 19: log likelihood = -3041.0223 (not concave)
Iteration 20: log likelihood = -3040.9861 (not concave)
Iteration 21: log likelihood = -3040.937 (not concave)
Iteration 22: log likelihood = -3040.8773 (not concave)
Iteration 23: log likelihood = -3040.8394 (not concave)
Iteration 24: log likelihood = -3040.7997 (not concave)
Iteration 25: log likelihood = -3039.9482 (not concave)
Iteration 26: log likelihood = -3039.5666 (not concave)
Iteration 27: log likelihood = -3039.2445 (not concave)
Iteration 28: log likelihood = -3038.9911 (not concave)
Iteration 29: log likelihood = -3038.865 (not concave)
Iteration 30: log likelihood = -3038.7521 (not concave)
Iteration 31: log likelihood = -3038.6483 (not concave)
Iteration 32: log likelihood = -3038.5445 (not concave)
Iteration 33: log likelihood = -3038.4473 (not concave)
Iteration 34: log likelihood = -3038.3563
Iteration 35: log likelihood = -3037.8615 (backed up)
Iteration 36: log likelihood = -3037.5189 (not concave)
Iteration 37: log likelihood = -3037.4274
Iteration 38: log likelihood = -3037.3026
Iteration 39: log likelihood = -3037.2661
Iteration 40: log likelihood = -3037.2659
Structural equation model Number of obs = 1122
Estimation method = ml
Log likelihood = -3037.2659
( 1) [alc1]Intercept = 1
( 2) [alc2]Intercept = 1
( 3) [alc2]Slope = .75
( 4) [alc3]Intercept = 1
( 5) [alc3]Slope = 1.75
( 6) [peer1]Int_peer = 1
( 7) [peer2]Int_peer = 1
( 8) [peer2]Slope_peer = .75
( 9) [peer3]Int_peer = 1
(10) [peer3]Slope_peer = 1.75
(11) [alc1]_cons = 0
(12) [alc2]_cons = 0
(13) [alc3]_cons = 0
(14) [peer1]_cons = 0
(15) [peer2]_cons = 0
(16) [peer3]_cons = 0
--------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
Structural |
Intercept <- |
Int_peer | .7985657 .1026103 7.78 0.000 .5974533 .9996782
Slope_peer | .0804674 .1836251 0.44 0.661 -.2794312 .440366
_cons | .0666213 .0156694 4.25 0.000 .0359099 .0973327
-------------+----------------------------------------------------------------
Slope <- |
Int_peer | -.1433222 .0760684 -1.88 0.060 -.2924134 .0057691
Slope_peer | .5766491 .1928544 2.99 0.003 .1986614 .9546368
_cons | .0083053 .0147289 0.56 0.573 -.0205628 .0371734
---------------+----------------------------------------------------------------
Measurement |
alc1 <- |
Intercept | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
alc2 <- |
Intercept | 1 (constrained)
Slope | .75 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
alc3 <- |
Intercept | 1 (constrained)
Slope | 1.75 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
peer1 <- |
Int_peer | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
peer2 <- |
Int_peer | 1 (constrained)
Slope_peer | .75 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
peer3 <- |
Int_peer | 1 (constrained)
Slope_peer | 1.75 (constrained)
_cons | 0 (constrained)
---------------+----------------------------------------------------------------
Mean |
Int_peer | .1881742 .0119528 15.74 0.000 .1647472 .2116012
Slope_peer | .0961698 .009693 9.92 0.000 .0771718 .1151678
---------------+----------------------------------------------------------------
Variance |
e.alc1 | .0480378 .00636 .0370586 .0622699
e.alc2 | .0762157 .0044401 .0679916 .0854345
e.alc3 | .0762793 .0097553 .0593672 .0980092
e.peer1 | .1057856 .0108049 .0865936 .1292313
e.peer2 | .1712812 .0086887 .1550708 .1891862
e.peer3 | .1289566 .0176032 .0986848 .1685143
e.Intercept | .0421663 .0074642 .0298049 .0596545
e.Slope | .009219 .0054309 .0029056 .0292501
Int_peer | .0696854 .0103557 .0520773 .0932472
Slope_peer | .0284745 .0088588 .0154751 .0523935
---------------+----------------------------------------------------------------
Covariance |
e.alc1 |
e.peer1 | .0109318 .0061648 1.77 0.076 -.0011509 .0230145
-------------+----------------------------------------------------------------
e.alc2 |
e.peer2 | .0339993 .004642 7.32 0.000 .0249012 .0430973
-------------+----------------------------------------------------------------
e.alc3 |
e.peer3 | .0374121 .0102133 3.66 0.000 .0173944 .0574298
-------------+----------------------------------------------------------------
e.Intercept |
e.Slope | -.0063657 .0050972 -1.25 0.212 -.0163562 .0036247
-------------+----------------------------------------------------------------
Int_peer |
Slope_peer | .0011757 .0070985 0.17 0.868 -.0127372 .0150885
--------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(4) = 11.56, Prob > chi2 = 0.0210
*save model results for comparison to baseline model
estimates store m1
*baseline model with 4 gamma parameters constrained equal to 0
sem (alc1 <- Intercept@1 Slope@0 _cons@0) ///
(alc2 <- Intercept@1 Slope@0.75 _cons@0) ///
(alc3 <- Intercept@1 Slope@1.75 _cons@0) ///
(peer1 <- Int_peer@1 Slope_peer@0 _cons@0) ///
(peer2 <- Int_peer@1 Slope_peer@0.75 _cons@0) ///
(peer3 <- Int_peer@1 Slope_peer@1.75 _cons@0) ///
(Intercept <- _cons) ///
(Slope <- _cons), ///
var(e.alc1 e.alc2 e.alc3 ///
e.peer1 e.peer2 e.peer3 ///
(Int_peer, init(.1)) ///
(Slope_peer, init(.1)) ///
(e.Intercept, init(.1)) ///
(e.Slope, init(.1))) ///
cov(e.alc1*e.peer1 e.alc2*e.peer2 e.alc3*e.peer3 ///
(e.Intercept*e.Slope, init(0)) ///
(Int_peer*Slope_peer, init(0))) ///
means((Int_peer, init(.2)) ///
(Slope_peer, init(.1)))
Endogenous variables
Measurement: alc1 alc2 alc3 peer1 peer2 peer3
Latent: Intercept Slope
Exogenous variables
Latent: Int_peer Slope_peer
Fitting target model:
Iteration 0: log likelihood = -4020.0749 (not concave)
Iteration 1: log likelihood = -3853.4641 (not concave)
Iteration 2: log likelihood = -3700.9751 (not concave)
Iteration 3: log likelihood = -3485.2731
Iteration 4: log likelihood = -3342.0888
Iteration 5: log likelihood = -3215.7272
Iteration 6: log likelihood = -3203.1443
Iteration 7: log likelihood = -3202.8119
Iteration 8: log likelihood = -3202.8112
Iteration 9: log likelihood = -3202.8112
Structural equation model Number of obs = 1122
Estimation method = ml
Log likelihood = -3202.8112
( 1) [alc1]Intercept = 1
( 2) [alc2]Intercept = 1
( 3) [alc2]Slope = .75
( 4) [alc3]Intercept = 1
( 5) [alc3]Slope = 1.75
( 6) [peer1]Int_peer = 1
( 7) [peer2]Int_peer = 1
( 8) [peer2]Slope_peer = .75
( 9) [peer3]Int_peer = 1
(10) [peer3]Slope_peer = 1.75
(11) [alc1]_cons = 0
(12) [alc2]_cons = 0
(13) [alc3]_cons = 0
(14) [peer1]_cons = 0
(15) [peer2]_cons = 0
(16) [peer3]_cons = 0
--------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
Structural |
Intercept <- |
_cons | .2259628 .0104291 21.67 0.000 .2055221 .2464035
-------------+----------------------------------------------------------------
Slope <- |
_cons | .037103 .0075926 4.89 0.000 .0222218 .0519841
---------------+----------------------------------------------------------------
Measurement |
alc1 <- |
Intercept | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
alc2 <- |
Intercept | 1 (constrained)
Slope | .75 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
alc3 <- |
Intercept | 1 (constrained)
Slope | 1.75 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
peer1 <- |
Int_peer | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
peer2 <- |
Int_peer | 1 (constrained)
Slope_peer | .75 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
peer3 <- |
Int_peer | 1 (constrained)
Slope_peer | 1.75 (constrained)
_cons | 0 (constrained)
---------------+----------------------------------------------------------------
Mean |
Int_peer | .1875529 .0116206 16.14 0.000 .1647769 .2103288
Slope_peer | .0964748 .0099423 9.70 0.000 .0769883 .1159613
---------------+----------------------------------------------------------------
Variance |
e.alc1 | .0534991 .0064425 .0422516 .0677408
e.alc2 | .0839361 .0045462 .0754824 .0933366
e.alc3 | .0963529 .009856 .0788488 .1177429
e.peer1 | .117066 .0112152 .0970249 .1412466
e.peer2 | .1895692 .0095278 .1717854 .2091941
e.peer3 | .1621563 .018436 .1297653 .2026325
e.Intercept | .07613 .0068187 .0638729 .0907392
e.Slope | .0161068 .0047646 .0090202 .0287611
Int_peer | .0507773 .0101429 .0343273 .0751103
Slope_peer | .0198255 .0083772 .0086607 .0453834
---------------+----------------------------------------------------------------
Covariance |
e.alc1 |
e.peer1 | .03488 .0041296 8.45 0.000 .0267862 .0429739
-------------+----------------------------------------------------------------
e.alc2 |
e.peer2 | .0430589 .0054281 7.93 0.000 .0324199 .0536978
-------------+----------------------------------------------------------------
e.alc3 |
e.peer3 | .0854598 .0065007 13.15 0.000 .0727187 .098201
-------------+----------------------------------------------------------------
e.Intercept |
e.Slope | -.0161889 .0042923 -3.77 0.000 -.0246017 -.0077761
-------------+----------------------------------------------------------------
Int_peer |
Slope_peer | -.0000703 .0065932 -0.01 0.991 -.0129928 .0128521
--------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(8) = 342.65, Prob > chi2 = 0.0000
*likelihood ratio test of change in chi-square
lrtest m1 .
Likelihood-ratio test LR chi2(4) = 331.09
(Assumption: . nested in m1) Prob > chi2 = 0.0000