将混合模型的结果保存在数据集中

Question

我正在拟合下面的混合模型：

. mixed y trt || clst:trt, nocons reml dfmethod(sat)

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log restricted-likelihood = -1295.3123  
Iteration 1:   log restricted-likelihood = -1295.3098  
Iteration 2:   log restricted-likelihood = -1295.3098  

Computing standard errors:

Computing degrees of freedom:

Mixed-effects REML regression                   Number of obs     =        919
Group variable: clst                            Number of groups  =         49

                                                Obs per group:
                                                              min =          1
                                                              avg =       18.8
                                                              max =         30
DF method: Satterthwaite                        DF:           min =     888.00
                                                              avg =     900.91
                                                              max =     913.83

                                                F(1,   913.83)    =       0.40
Log restricted-likelihood = -1295.3098          Prob > F          =     0.5251

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         trt |   .1455914   .2290005     0.64   0.525    -.3038366    .5950193
       _cons |   .3951269   .2241477     1.76   0.078    -.0447941     .835048
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
clst: Identity               |
                    var(trt) |   .0341507   .0173905      .0125877     .092652
-----------------------------+------------------------------------------------
               var(Residual) |   .9546016   .0453034      .8698131    1.047655
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 9.46          Prob >= chibar2 = 0.0010

. return list

scalars:
              r(level) =  95

matrices:
              r(table) :  9 x 4

接下来，我按如下方式计算ICC：

. nlcom (icc_est: (exp(_b[lns1_1_1:_cons])^2)/((exp(_b[lns1_1_1:_cons])^2)+(exp(_b[lnsig_e:_cons])^2)))

     icc_est:  (exp(_b[lns1_1_1:_cons])^2)/((exp(_b[lns1_1_1:_cons])^2)+(exp(_b[lnsig_e:_cons])^2))

------------------------------------------------------------------------------
           y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     icc_est |   .0345392   .0171907     2.01   0.045     .0008461    .0682323
------------------------------------------------------------------------------

如何将结果保存到数据集中？

我希望所有三个表都显示出来:固定效果，随机效果和ICC结果。

Answer 1

考虑使用Stata的pig玩具数据集的以下可重现的示例：

webuse pig, clear

mixed weight week || id:week, nocons reml dfmethod(sat)

nlcom (icc_est: (exp(_b[lns1_1_1:_cons])^2)/((exp(_b[lns1_1_1:_cons])^2)+(exp(_b[lnsig_e:_cons])^2))), post

------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
     icc_est |   .1380299   .0265754     5.19   0.000     .0859431    .1901167
------------------------------------------------------------------------------

下面的方法对我很有效：

generate double coef = _b[icc_est]
generate double se = _se[icc_est]

generate p = string(2 * (normal(-(_b[icc_est] / _se[icc_est]))), "%9.3f")

generate double upper = _b[icc_est] + _se[icc_est] * invnormal(0.025)
generate double lower = _b[icc_est] + _se[icc_est] * invnormal(0.975)

list coef se p upper lower in 1 

     +-------------------------------------------------------+
     |      coef          se       p       upper       lower |
     |-------------------------------------------------------|
  1. | .13802987   .02657538   0.000   .08594308   .19011667 |
     +-------------------------------------------------------+

save mydata.dta

对于主模型的结果，该过程类似。

Answer 2

作为后续，很容易获得随机截距方差和SE、残差方差和SE将需要更多的代码行。但正如前面的答复所指出的，主模型的结果是以与ICC结果相同的方式获得的。请参阅下面的代码。

mixed y trt || clst:trt, nocons reml dfmethod(sat)
gen double fixedcoef = _b[trt]
gen double fixedse = _se[trt]

_diparm lns1_1_1, f(exp(@)^2) d(2*exp(@)^2)
gen double randomcoef = r(est)
gen double randomse = r(se)

_diparm lnsig_e, f(exp(@)^2) d(2*exp(@)^2)
gen double residcoef = r(est)
gen double residse = r(se)