Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
| From | Joseph Trubisz <jtrubisz@mac.com> |
| To | statalist@hsphsun2.harvard.edu |
| Subject | st: Odd SEM Results |
| Date | Sat, 03 Aug 2013 12:38:54 -0400 |
Greetings...
I probably am just missing something, but I don't know what.
I'm attempting to use sembuilder to create the diagram from Acock's SEM book, specifically the example
as shown on p.188.
If I use sembuilder, it generates the following output:
. sem (Intercept@1 -> bmi01) (Intercept@1 -> bmi02) (Intercept@1 -> bmi03) (Intercept@1 -> bmi05) (Interc
> ept@1 -> bmi06) (Intercept@1 -> bmi07) (Intercept@1 -> bmi08) (Intercept@1 -> bmi09) (Slope@0 -> bmi01)
> (Slope@1 -> bmi02) (Slope@2 -> bmi03) (Slope@4 -> bmi05) (Slope@5 -> bmi06) (Slope@6 -> bmi07) (Slope@
> 7 -> bmi08) (Slope@8 -> bmi09) (_cons -> Intercept) (_cons -> Slope) (male -> Intercept) (male -> Slope
> ) (wgtc -> Intercept) (wgtc -> Slope) if bmi01!=.|bmi02!=.|bmi03!=.|bmi05!=.|bmi06!=.|bmi07!=.|bmi08!=.
> |bmi09!=., method(mlmv) latent(Intercept Slope ) var( e.Intercept*e.Slope) nocapslatent noconstant
note: Missing values found in observed exogenous variables. Using the noxconditional behavior. Specify
the forcexconditional option to override this behavior.
Endogenous variables
Measurement: bmi01 bmi02 bmi03 bmi05 bmi06 bmi07 bmi08 bmi09
Latent: Intercept Slope
Exogenous variables
Observed: male wgtc
Fitting saturated model:
Iteration 0: log likelihood = -30162.223
Iteration 1: log likelihood = -29297.714
Iteration 2: log likelihood = -28707.587
Iteration 3: log likelihood = -28564.929
Iteration 4: log likelihood = -28557.353
Iteration 5: log likelihood = -28557.204
Iteration 6: log likelihood = -28557.204
Fitting baseline model:
Iteration 0: log likelihood = -36523.419
Iteration 1: log likelihood = -36520.845
Iteration 2: log likelihood = -36520.836
Iteration 3: log likelihood = -36520.836
Fitting target model:
Iteration 0: log likelihood = -52919.48 (not concave)
Iteration 1: log likelihood = -52675.161 (not concave)
Iteration 2: log likelihood = -52171.219 (not concave)
Iteration 3: log likelihood = -49397.835 (not concave)
Iteration 4: log likelihood = -42220.623 (not concave)
Iteration 5: log likelihood = -39274.796
Iteration 6: log likelihood = -38652.54
Iteration 7: log likelihood = -34772.666
Iteration 8: log likelihood = -32169.128
Iteration 9: log likelihood = -31367.639
Iteration 10: log likelihood = -30934.922
Iteration 11: log likelihood = -30910.018
Iteration 12: log likelihood = -30909.236
Iteration 13: log likelihood = -30909.234
Structural equation model Number of obs = 1581
Estimation method = mlmv
Log likelihood = -30909.234
( 1) [bmi01]Intercept = 1
( 2) [bmi02]Intercept = 1
( 3) [bmi02]Slope = 1
( 4) [bmi03]Intercept = 1
( 5) [bmi03]Slope = 2
( 6) [bmi05]Intercept = 1
( 7) [bmi05]Slope = 4
( 8) [bmi06]Intercept = 1
( 9) [bmi06]Slope = 5
(10) [bmi07]Intercept = 1
(11) [bmi07]Slope = 6
(12) [bmi08]Intercept = 1
(13) [bmi08]Slope = 7
(14) [bmi09]Intercept = 1
(15) [bmi09]Slope = 8
(16) [bmi01]_cons = 0
(17) [bmi02]_cons = 0
(18) [bmi03]_cons = 0
(19) [bmi05]_cons = 0
(20) [bmi06]_cons = 0
(21) [bmi07]_cons = 0
(22) [bmi08]_cons = 0
(23) [bmi09]_cons = 0
--------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
Structural |
Intercept <- |
male | 26.90551 .6496371 41.42 0.000 25.63225 28.17878
wgtc | 6.996785 .6003745 11.65 0.000 5.820072 8.173497
-------------+----------------------------------------------------------------
Slope <- |
male | .3657208 .02104 17.38 0.000 .3244831 .4069584
wgtc | .0889063 .0194718 4.57 0.000 .0507423 .1270703
---------------+----------------------------------------------------------------
Measurement |
bmi01 <- |
Intercept | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi02 <- |
Intercept | 1 (constrained)
Slope | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi03 <- |
Intercept | 1 (constrained)
Slope | 2 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi05 <- |
Intercept | 1 (constrained)
Slope | 4 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi06 <- |
Intercept | 1 (constrained)
Slope | 5 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi07 <- |
Intercept | 1 (constrained)
Slope | 6 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi08 <- |
Intercept | 1 (constrained)
Slope | 7 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi09 <- |
Intercept | 1 (constrained)
Slope | 8 (constrained)
_cons | 0 (constrained)
---------------+----------------------------------------------------------------
Mean |
male | .4990512 .0125749 39.69 0.000 .474405 .5236975
wgtc | -.0001655 .0192488 -0.01 0.993 -.0378925 .0375614
---------------+----------------------------------------------------------------
Variance |
e.bmi01 | 2.618815 .2046736 2.246876 3.052323
e.bmi02 | 4.086149 .2175673 3.681222 4.535619
e.bmi03 | 4.674361 .2320231 4.241024 5.151974
e.bmi05 | 5.778033 .2604252 5.289505 6.311681
e.bmi06 | 8.181968 .3511234 7.521926 8.899928
e.bmi07 | 3.794672 .1906747 3.438769 4.187409
e.bmi08 | 2.92201 .1689406 2.608965 3.272618
e.bmi09 | 3.308288 .2088301 2.923295 3.743984
e.Intercept | 324.2532 11.61219 302.2741 347.8304
e.Slope | .2448578 .0117764 .2228311 .269062
male | .2499991 .0088918 .2331651 .2680484
wgtc | .5851282 .0208269 .5456996 .6274057
---------------+----------------------------------------------------------------
Covariance |
e.Intercept |
e.Slope | 4.109434 .280162 14.67 0.000 3.560327 4.658542
-------------+----------------------------------------------------------------
male |
wgtc | -.0751429 .0098082 -7.66 0.000 -.0943666 -.0559193
--------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(45) = 4704.06, Prob > chi2 = 0.0000
However, the output is nothing like what's in the book. However, if I type in
exactly what's in the book (p.188), I get the correct results as shown below:
. sem (Intercept@1 Slope@0->bmi01) (Intercept@1 Slope@1->bmi02) (Intercept@1 Slope@2->bmi03) (Intercept@1
> Slope@4->bmi05)(Intercept@1 Slope@5->bmi06)(Intercept@1 Slope@6->bmi07)(Intercept@1 Slope@7->bmi08)(In
> tercept@1 Slope@8->bmi09) (Intercept Slope<-male wgtc _cons) if bmi01!=.|bmi02!=.|bmi03!=.|bmi05!=.|bmi
> 06!=.|bmi07!=.|bmi08!=.|bmi09!=.,var(e.Intercept*e.Slope) method(mlmv) noconstant
note: Missing values found in observed exogenous variables. Using the noxconditional behavior. Specify
the forcexconditional option to override this behavior.
Endogenous variables
Measurement: bmi01 bmi02 bmi03 bmi05 bmi06 bmi07 bmi08 bmi09
Latent: Intercept Slope
Exogenous variables
Observed: male wgtc
Fitting saturated model:
Iteration 0: log likelihood = -30162.223
Iteration 1: log likelihood = -29297.714
Iteration 2: log likelihood = -28707.587
Iteration 3: log likelihood = -28564.929
Iteration 4: log likelihood = -28557.353
Iteration 5: log likelihood = -28557.204
Iteration 6: log likelihood = -28557.204
Fitting baseline model:
Iteration 0: log likelihood = -36523.419
Iteration 1: log likelihood = -36520.845
Iteration 2: log likelihood = -36520.836
Iteration 3: log likelihood = -36520.836
Fitting target model:
Iteration 0: log likelihood = -52919.48 (not concave)
Iteration 1: log likelihood = -52663.873 (not concave)
Iteration 2: log likelihood = -52499.164 (not concave)
Iteration 3: log likelihood = -52371.927 (not concave)
Iteration 4: log likelihood = -46362.021 (not concave)
Iteration 5: log likelihood = -34630.285 (not concave)
Iteration 6: log likelihood = -34303.836 (not concave)
Iteration 7: log likelihood = -29724.362
Iteration 8: log likelihood = -29095.8
Iteration 9: log likelihood = -28787.969
Iteration 10: log likelihood = -28750.647
Iteration 11: log likelihood = -28750.02
Iteration 12: log likelihood = -28750.019
Structural equation model Number of obs = 1581
Estimation method = mlmv
Log likelihood = -28750.019
( 1) [bmi01]Intercept = 1
( 2) [bmi02]Intercept = 1
( 3) [bmi02]Slope = 1
( 4) [bmi03]Intercept = 1
( 5) [bmi03]Slope = 2
( 6) [bmi05]Intercept = 1
( 7) [bmi05]Slope = 4
( 8) [bmi06]Intercept = 1
( 9) [bmi06]Slope = 5
(10) [bmi07]Intercept = 1
(11) [bmi07]Slope = 6
(12) [bmi08]Intercept = 1
(13) [bmi08]Slope = 7
(14) [bmi09]Intercept = 1
(15) [bmi09]Slope = 8
(16) [bmi01]_cons = 0
(17) [bmi02]_cons = 0
(18) [bmi03]_cons = 0
(19) [bmi05]_cons = 0
(20) [bmi06]_cons = 0
(21) [bmi07]_cons = 0
(22) [bmi08]_cons = 0
(23) [bmi09]_cons = 0
--------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
Structural |
Intercept <- |
male | 1.555545 .2436739 6.38 0.000 1.077953 2.033137
wgtc | 3.759441 .160021 23.49 0.000 3.445806 4.073077
_cons | 24.85781 .170538 145.76 0.000 24.52356 25.19206
-------------+----------------------------------------------------------------
Slope <- |
male | .0173321 .0271012 0.64 0.522 -.0357853 .0704495
wgtc | .0459197 .0178505 2.57 0.010 .0109333 .0809062
_cons | .3430045 .0189564 18.09 0.000 .3058506 .3801584
---------------+----------------------------------------------------------------
Measurement |
bmi01 <- |
Intercept | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi02 <- |
Intercept | 1 (constrained)
Slope | 1 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi03 <- |
Intercept | 1 (constrained)
Slope | 2 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi05 <- |
Intercept | 1 (constrained)
Slope | 4 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi06 <- |
Intercept | 1 (constrained)
Slope | 5 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi07 <- |
Intercept | 1 (constrained)
Slope | 6 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi08 <- |
Intercept | 1 (constrained)
Slope | 7 (constrained)
_cons | 0 (constrained)
-------------+----------------------------------------------------------------
bmi09 <- |
Intercept | 1 (constrained)
Slope | 8 (constrained)
_cons | 0 (constrained)
---------------+----------------------------------------------------------------
Mean |
male | .4990512 .0125749 39.69 0.000 .474405 .5236975
wgtc | -.0009276 .0192434 -0.05 0.962 -.0386439 .0367888
---------------+----------------------------------------------------------------
Variance |
e.bmi01 | 2.443884 .191981 2.095144 2.850673
e.bmi02 | 4.1472 .2157233 3.745229 4.592315
e.bmi03 | 4.772852 .2325326 4.33818 5.251077
e.bmi05 | 5.789807 .2596509 5.302625 6.321749
e.bmi06 | 8.228898 .3520222 7.56708 8.948599
e.bmi07 | 3.810727 .1909167 3.454322 4.203904
e.bmi08 | 2.922193 .1687984 2.609396 3.272487
e.bmi09 | 3.298484 .2089123 2.913418 3.734444
e.Intercept | 20.5145 .8043478 18.99706 22.15315
e.Slope | .189059 .0097265 .170925 .2091168
male | .2499991 .0088918 .2331651 .2680484
wgtc | .5849257 .0208131 .5455228 .6271748
---------------+----------------------------------------------------------------
Covariance |
e.Intercept |
e.Slope | -.0272943 .0635517 -0.43 0.668 -.1518533 .0972647
-------------+----------------------------------------------------------------
male |
wgtc | -.0747626 .0098037 -7.63 0.000 -.0939775 -.0555478
--------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(43) = 385.63, Prob > chi2 = 0.0000
Problem: I look at the command not working and comparing it to the command that does
work and I don't see the difference.
Can anyone point out to me where I might be going wrong?
TIA,
Joe
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/