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From |
duncan zheng <[email protected]> |

To |
[email protected] |

Subject |
st: a question for the multivariate random coefficients model using GLLAMM |

Date |
Thu, 14 Jan 2010 09:15:37 +0800 |

hi Statalist, I've found that in many books and papers, examples about the random-coefficient model using 'gllamm' only include only one variable allowed for random coefficient. I want to build up a model including more than one variable allowed for the random coefficient. For example, according to Sophia & Anders' book Multilevel and Longitudinal Modeling Using Stata P146-158, my model should be Yij=β1+β2 lrt+β3schav+ξ1j+ξ2j lrt+ξ3j schav+εij This includes two variables 'lrt' and 'schav'. I assume that the covariance matrix was τ= ■(ψ11&ψ12&ψ13@ψ21&ψ22&ψ23@ψ31&ψ32&ψ33) = ■(var(ξ1j|lra,schav)&cov(ξ1j,ξ2j|lra,schav)&cov(ξ1j,ξ3j|lra,schav)@cov(ξ2j,ξ1j|lra,schav)&var(ξ2j|lra,schav)&cov(ξ2j,ξ3j|lra,schav)@cov(ξ3j,ξ1j|lra,schav)&cov(ξ3j,ξ2j|lra,schav)&var(ξ3j|lra,schav)) I set equations for inter and slope respectively as follows: . gen cons=1 . eq inter:cons . eq slope:lrt schav To finish program "gllamm gcse lrt schav,i(school) nrf(2) eqs(inter slope) ip(m) nip(15) adapt", having no set the star values for computing, the result I got was: gllamm gcse lrt schav,i(school) nrf(2) eqs(inter slope) ip(m) nip(15) adapt number of level 1 units = 4059 number of level 2 units = 65 Condition Number = 317.56356 gllamm model log likelihood = -14002.461 gcse Coef. Std. Err. z P>z [95% Conf. Interval] lrt .5536729 .0200091 27.67 0.000 .5144558 .59289 schav 1.202866 .5645719 2.13 0.033 .0963259 2.309407 _cons -2.658444 1.208787 -2.20 0.028 -5.027622 -.2892659 Variance at level 1 55.370349 (1.2494905) Variances and covariances of random effects ***level 2 (school) var(1): 6.6744527 (1.5422225) cov(2,1): .02919465 (.12183525) cor(2,1): .09411644 var(2): .01441651 (.00459126) loadings for random effect 2 lrt: 1 (fixed) schav: 3.0914009 (3.5706898) As was shown above, I can't point out var(ξ1j|lra,schav), var(ξ2j|lra,schav) and var(ξ3j|lra,schav). I can't figure out all co-variances respectively, and I don't understand the last three red lines' meaning exactly. Can you give me further explanation about these? Another question is how I can set the star values for computing. As below: . gllamm gcse lrt schav,i(school) adapt . estimates store rig . gen cons=1 . eq inter:cons . eq slope:lrt schav . matrix a=e(b). . matrix a=(a,0,0,0) . gllamm gcse lrt schav,i(school) nrf(2) eqs(inter slope) ip(m) nip(15) adapt from(a) copy Running adaptive quadrature Iteration 0: log likelihood = -14020.987 (error occurred in ML computation) (use trace option and check correctness of initial model) I think there should be something wrong with my setting value. Whereas when I set the star values as matrix a=(a,.5,.1,.2), the program finished successfully. The result show that . gllamm gcse lrt schav,i(school) nrf(2) eqs(inter slope) ip(m) nip(15) adapt from(a) copy log likelihood = -14002.503 gcse Coef. Std. Err. z P>z [95% Conf. Interval] lrt .5536594 .020099 27.55 0.000 .514266 .5930527 schav 1.170598 .5637334 2.08 0.038 .0657008 2.275495 _cons -2.554637 1.216821 -2.10 0.036 -4.939563 -.1697109 Variance at level 1 55.364746 (1.2492625) Variances and covariances of random effects ***level 2 (school) var(1): 7.2082699 (1.8332009) cov(2,1): .09016604 (.09988308) cor(2,1): .27814664 var(2): .01457831 (.00462039) loadings for random effect 2 lrt: 1 (fixed) schav: 1.2700516 (2.5486213) Thanks a lot. Sincerely yours, Zheng Dachuan 2010/1/14 * * 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/

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