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Re: st: Odd SEM Results
From
Anders Alexandersson <[email protected]>
To
[email protected]
Subject
Re: st: Odd SEM Results
Date
Mon, 5 Aug 2013 15:30:28 -0400
Can you simplify the model (e.g., drop all the covariates), put
everything in a reproducible do-file, and still get different results?
That would greatly simplify the problem. If you provide a reproducible
do-file, I and maybe others would try it and report back.
Alternatively, you could contact StataCorp since -sem- is an official
command and therefore supported.
Anders Alexandersson
[email protected]
On Mon, Aug 5, 2013 at 1:46 PM, Joseph Trubisz <[email protected]> wrote:
> Tried that as well.
> Exact same error.
>
> Joe
>
>
> On Aug 5, 2013, at 11:13 AM, Anders Alexandersson <[email protected]> wrote:
>
>> Hi Joe, only the first model from the SEM builder specifies the
>> options "latent(Intercept Slope ) nocapslatent" which, I guess, might
>> be why this model has chi2(45) instead of chi2(43) and no output for
>> _cons in the structural model. What happens if you instead use the
>> default?
>>
>> Anders Alexandersson
>> [email protected]
>>
>> On Sat, Aug 3, 2013 at 12:38 PM, Joseph Trubisz <[email protected]> wrote:
>>> 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?
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