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From |
Scott Baldwin <baldwinlist@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Multiple group latent growth models with xtmixed |

Date |
Mon, 23 Nov 2009 13:16:11 -0700 |

John Holmes asked: "I have been estimating latent growth models using -xtmixed- with a continuous income measure as the dependent variable and long-term family status (14 categories) as an independent variable. However, I am wanting to estimate simultaneous models for two separate groups - those poor at time=0 and those not poor at time=0. Is this possible using xtmixed?" You can. Given that you are calling your analysis a latent growth model, I assume you are coming from SEM framework. Anyhow, you can fit the mixed model version of a multiple group model using a separate intercepts separate slopes model. Note, however, this is equivalent to including the grouping variable as a main effect and as an interaction with the time variable. It is just a reparamaterization of the typical interaction model. In any case, the following code using the "childweight" data described in the xtmixed documentation will illustrate what you have to do. Hope this helps. Best, Scott ****************************** use http://www.stata-press.com/data/r11/childweight, clear *fit the typical interaction model gen agegirl=age*girl xtmixed weight age girl agegirl || id: age, var cov(un) ml ------------------------------------------------------------------------------ weight | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | 3.575854 .1770211 20.20 0.000 3.228899 3.922809 girl | -.4639727 .2933195 -1.58 0.114 -1.038868 .1109229 agegirl | -.2358053 .2501978 -0.94 0.346 -.726184 .2545733 _cons | 5.345483 .2063143 25.91 0.000 4.941114 5.749851 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Unstructured | var(age) | .1988519 .1258059 .0575449 .6871511 var(_cons) | .0540533 .0844623 .0025279 1.155816 cov(age,_cons) | .103675 .0602908 -.0144927 .2218428 -----------------------------+------------------------------------------------ var(Residual) | 1.350626 .1634692 1.065399 1.712214 ------------------------------------------------------------------------------ *log-likelihood display(e(ll)) -338.6593 *note that the _cons is equivalent to the boys' intercept when age equals zero and the the coefficient for girl is the difference between the boys' intercept and the girls' intercepts. Likewise, the age coefficient is the slope for boys and the age x girl interaction is the difference between the boys' and girls' slopes. *fit the separate intercepts and slopes model. You need to create indicator variables for each level of your grouping variable. We're going include both and suppress the intercept. So I will create two new dummy variables called male (1 for boys, 0 for girls) and female (1 for girls, 0 for boys). We're also going to create the interaction between the new dummy variables and the age variable. We aren't going to include a main effect for age. tab girl, gen(sex) rename sex1 male rename sex2 female gen agemale=age*male gen agefemale=age*female xtmixed weight male agemale female agefemale, nocons || id:age, var cov(un) ml ------------------------------------------------------------------------------ weight | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- male | 5.345483 .2063144 25.91 0.000 4.941115 5.749852 agemale | 3.575854 .1770212 20.20 0.000 3.228899 3.922809 female | 4.88151 .2084964 23.41 0.000 4.472865 5.290156 agefemale | 3.340048 .1768121 18.89 0.000 2.993503 3.686594 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ id: Unstructured | var(age) | .1988517 .1258081 .0575436 .687166 var(_cons) | .0540512 .0844627 .0025274 1.155925 cov(age,_cons) | .103673 .0602922 -.0144975 .2218435 -----------------------------+------------------------------------------------ var(Residual) | 1.35063 .1634699 1.065402 1.712219 ------------------------------------------------------------------------------ *log-likelihood display(e(ll)) -338.6593 *note that the fit is identical to the previous model. We just reparamaterized things. The male and female coefficients are the intercepts for males and females, respectively. The agemale and agefemale coefficients are the slopes for age for males and females, respectively. *With a little arithmetic, you can move between the coefficients in this model and the coefficients in the previous model. For example, to get the coefficient for girl from the first model take the difference between the female and male coefficients. display 4.88151-5.345483 -.463716 *You can also estimate separate random effects across groups xtmixed weight male agemale female agefemale, nocons || id:male agemale, nocons cov(un) || id:female agefemale, nocons var cov(un) ml *And if you have Stata 11, you can easily estimate separate residual errors. It is more complicated in Stata 10. xtmixed weight male agemale female agefemale, nocons || id:male agemale, nocons cov(un) || id:female agefemale, nocons var cov(un) residuals(independent, by(girl)) ml *Finally, you could use an likelihood ratio-test to see if fitting separate intercepts and slopes improves model fit. xtmixed weight age || id:age, var cov(un) ml estimates store model1 xtmixed weight male agemale female agefemale, nocons || id:age, var cov(un) ml estimates store model2 lrtest model1 model2 * * 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/

**Follow-Ups**:**Re: st: Multiple group latent growth models with xtmixed***From:*John Holmes <johnholmesyork@googlemail.com>

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