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From |
Stas Kolenikov <skolenik@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: GLLAMM |

Date |
Fri, 12 Feb 2010 14:24:28 -0600 |

On Fri, Feb 12, 2010 at 9:33 AM, arosella <arosella@irccsdebellis.it> wrote: > Dear Stata Listers, I am trying to estimate a model using GLLAMM. I have an > unobserved response variable (performance) that is comprised of three > numerical items (perf1, perf2,perf3). This unobserved response shoul be > regressed on two latent variables (iute, iure) each comprised of four and > two numerical items respectively. There are moreover eight covariates with > direct effect on unobserved response variable and on both iure and iute. I > have written some code (and have pasted it below) to build a responde > variable but I cannot figure out how to construc the DV as latent variable > and the other latent variable. It's too complicated for me. Does anyone have > any advice? Any help would be greatly appreciated. > I'll give you some building blocks to work with. ======== beginning of sample code ========= // Let the indicators of the performance be perf1 - perf3 gen resp1 = perf1 gen resp2 = perf2 gen resp3 = perf3 // Let the indicators of iute be iute1, iute2 gen resp4 = iute1 gen resp5 = iute2 // Let the indicators of iure be iure1 - iure4 gen resp6 = iure1 gen resp7 = iure2 gen resp8 = iure3 gen resp9 = iure4 // reshape to gllamm gen i = _n reshape long resp, i(i) j(j 1-9) tab j, gen( v ) // you now have variables v1-v9 corresponding to 9 indicators of all the latents // measurement equations eq f1 : v1 v2 v3 eq f2 : v4 v5 eq f3 : v6 v7 v8 v9 // exogenous variables affecting the latent variables: x1 - x8 eq r1 : x1 x2 x3 x4 x5 x6 x7 x8 eq r2 : x1 x2 x3 x4 x5 x6 x7 x8 eq r3 : x1 x2 x3 x4 x5 x6 x7 x8 // define the regression of latent variable on one another mat B = (0, 1, 1 \ 0, 0, 0 \ 0, 0, 0) // BIG BANG gllamm resp v*, i(i) nrf(3) eq( f1 f2 f3 ) geq( r1 r2 r3 ) bmat( B ) ======== end of sample code ========= No warranties that this will run, just some guidelines. It will take a very long time to converge, given the number of parameters. If you have categorical indicators, then you'd want to use -family() fv() link() lv()- options to fully utilize the infinite powers of -gllamm- :)) If you are interested, I have a working paper in progress where I show how to estimate structural equation models using -gmm-. That's much faster than in -gllamm-, although requires some custom coding, too. -- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: I use this email account for mailing lists only. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: GLLAMM***From:*"arosella" <arosella@irccsdebellis.it>

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