st: interpret output from mim & xtmixed

 From Frank Gallo To statalist@hsphsun2.harvard.edu Subject st: interpret output from mim & xtmixed Date Sun, 09 Aug 2009 08:34:43 -0400

```Hi All,

```
I am a beginner who is using Stata Version 11. Below is output for a Random-Intercept Model: (1) the original data set, and (2) 5 imputations. What I would like to know is how to interpret output from -mim: xtmixed- before I begin my analyses on the imputations. I found a thread in the archives, but it did not address my question. I understand what the output shows from -xtmixed- only. I do not understand the random effects part of the -mim: xtmixed- output. I would greatly appreciate any resources and/or suggestions. Thank you.
```
Best,
Frank

xtmixed pforce if _mj==0 || pd:, mle variance

Performing EM optimization:

Iteration 0:   log likelihood = -3790.7576
Iteration 1:   log likelihood = -3790.7576

Computing standard errors:

```
Mixed-effects ML regression Number of obs = 3300 Group variable: pd Number of groups = 16
```
```
Obs per group: min = 22 avg = 206.2 max = 696
```

```
Wald chi2(0) = . Log likelihood = -3790.7576 Prob > chi2 = .
```
------------------------------------------------------------------------------
```
pforce | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------------- +---------------------------------------------------------------- _cons | 3.365989 .0380829 88.39 0.000 3.291348 3.44063
```------------------------------------------------------------------------------

------------------------------------------------------------------------------
```
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] ----------------------------- +------------------------------------------------
```pd: Identity                 |
```
var(_cons) | .0177083 .0073768 . 0078269 .040065 ----------------------------- +------------------------------------------------ var(Residual) | .5776353 .0142484 . 5503734 .6062477
```------------------------------------------------------------------------------
```
LR test vs. linear regression: chibar2(01) = 102.86 Prob >= chibar2 = 0.0000
```

mim: xtmixed pforce || pd:, mle variance

```
Multiple-imputation estimates (xtmixed) Imputations = 5 Mixed-effects ML regression Minimum obs = 3300 Minimum dof = 975.1
```
------------------------------------------------------------------------------
```
pforce | Coef. Std. Err. t P>|t| [95% Conf. Int.] MI.df ------------- +---------------------------------------------------------------- _cons | 3.36599 .038083 88.39 0.000 3.29126 3.44072 997.0 ------------- +---------------------------------------------------------------- /lns1_1_1 | -2.01686 .208286 -2.42559 -1.60813 998.0 /lnsig_e | -.274406 .012333 -.298609 -. 250203 975.1
```------------------------------------------------------------------------------

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