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| From | Anders Alexandersson <andersalex@gmail.com> |
| To | statalist@hsphsun2.harvard.edu |
| 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 andersalex@gmail.com On Mon, Aug 5, 2013 at 1:46 PM, Joseph Trubisz <jtrubisz@me.com> wrote: > Tried that as well. > Exact same error. > > Joe > > > On Aug 5, 2013, at 11:13 AM, Anders Alexandersson <andersalex@gmail.com> 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 >> andersalex@gmail.com >> >> On Sat, Aug 3, 2013 at 12:38 PM, Joseph Trubisz <jtrubisz@mac.com> 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? >> * >> * 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/ > > > * > * 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/ * * 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/