use https://www.bristol.ac.uk/cmm/media/runmlwin/tutorial, clearreffadjustsim helpfile examples
Examples 1 & 2 assume the path to the MLwiN executable is set in global MLwiN_path; see help runmlwin
Example 1: Two level continuous response model
(see page 59 of the MLwiN User Manual)
- read in data
- fit model using MLwiN via
runmlwin
runmlwin normexam cons standlrt, level1(student: cons) ///
level2(school: cons standlrt) batch
--- Begin MLwiN error log ---
MLN - Software for N-level analysis. Mon 20 Oct 2025 08:54:48
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000003
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000007
--- End MLwiN error log ---
Warning: getversion plugin could not be loaded
MLwiN 3.16 multilevel model Number of obs = 4059
Normal response model (hierarchical)
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
school | 65 2 62.4 198
-----------------------------------------------------------
Run time (seconds) = 0.06
Number of iterations = 4
Log likelihood = -4658.435
Deviance = 9316.87
------------------------------------------------------------------------------
normexam | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | -.0115051 .039783 -0.29 0.772 -.0894783 .066468
standlrt | .5567305 .019937 27.92 0.000 .5176547 .5958062
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: school |
var(cons) | .0904446 .017924 .0553142 .1255749
cov(cons,standlrt) | .0180414 .0067229 .0048649 .031218
var(standlrt) | .0145361 .0044139 .0058851 .0231872
-----------------------------+------------------------------------------------
Level 1: student |
var(cons) | .5536575 .0124818 .5291937 .5781214
------------------------------------------------------------------------------/nogui option ignored
ECHO 0
Execution completed- report coefficient as median with 2.5 & 97.5 percentiles
reffadjustsim cons standlrt, eqn(RP2) seed(12345)
------------------------------------------------------------------------------
cons | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
standlrt | 1.242541 .4311653 2.607229
------------------------------------------------------------------------------- report coefficient as mean & Wald-type confidence interval
- Warning: mean and Wald-type confidence are inaccurate in this example
reffadjustsim cons standlrt, eqn(RP2) seed(12345) waldtype
------------------------------------------------------------------------------
cons | Coef. Std. Err. [95% Conf. Interval]
-------------+----------------------------------------------------------------
standlrt | 1.319134 1.22225 -1.076431 3.7147
------------------------------------------------------------------------------
Warning: Coef. & Wald-type conf. interval limits may be inaccurate.
Please compare with default output which reports median & centiles.- compare with delta-method confidence interval (first refit model)
runmlwin normexam cons standlrt, level1(student: cons) ///
level2(school: cons standlrt) batch
reffadjust4nlcom cons standlrt, eqn(RP2)
nlcom `r(beta_standlrt)'
--- Begin MLwiN error log ---
MLN - Software for N-level analysis. Mon 20 Oct 2025 08:54:48
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000004
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000008
--- End MLwiN error log ---
Warning: getversion plugin could not be loaded
MLwiN 3.16 multilevel model Number of obs = 4059
Normal response model (hierarchical)
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
school | 65 2 62.4 198
-----------------------------------------------------------
Run time (seconds) = 0.04
Number of iterations = 4
Log likelihood = -4658.435
Deviance = 9316.87
------------------------------------------------------------------------------
normexam | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | -.0115051 .039783 -0.29 0.772 -.0894783 .066468
standlrt | .5567305 .019937 27.92 0.000 .5176547 .5958062
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: school |
var(cons) | .0904446 .017924 .0553142 .1255749
cov(cons,standlrt) | .0180414 .0067229 .0048649 .031218
var(standlrt) | .0145361 .0044139 .0058851 .0231872
-----------------------------+------------------------------------------------
Level 1: student |
var(cons) | .5536575 .0124818 .5291937 .5781214
------------------------------------------------------------------------------
_nl_1: [RP2]cov(cons\standlrt)/[RP2]var(standlrt)
------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
_nl_1 | 1.241144 .4275471 2.90 0.004 .4031675 2.079121
------------------------------------------------------------------------------/nogui option ignored
ECHO 0
Execution completed- compare with Bayesian posterior distribution
runmlwin normexam cons standlrt, level1(student: cons) ///
level2(school: cons standlrt) batch mcmc(on) initsprevious seed(121211)
mcmcsum, getchains
reffadjustsim cons standlrt, eqn(RP2) mcmcsum /nogui option ignored
ECHO 0
BURNING IN...
Burning in for 50 iterations out of 500
Burning in for 100 iterations out of 500
Burning in for 150 iterations out of 500
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Actual update 4800 of 5000, Stored update 4800 of 5000 --- Begin MLwiN error log ---
MLN - Software for N-level analysis. Mon 20 Oct 2025 08:54:48
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000006
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.00000a
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000004
--- End MLwiN error log ---
Actual update 4850 of 5000, Stored update 4850 of 5000
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Execution completedWarning: getversion plugin could not be loaded
MLwiN 3.16 multilevel model Number of obs = 4059
Normal response model (hierarchical)
Estimation algorithm: MCMC
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
school | 65 2 62.4 198
-----------------------------------------------------------
Burnin = 500
Chain = 5000
Thinning = 1
Run time (seconds) = .627
Deviance (dbar) = 9122.42
Deviance (thetabar) = 9031.09
Effective no. of pars (pd) = 91.34
Bayesian DIC = 9213.76
------------------------------------------------------------------------------
normexam | Mean Std. Dev. ESS P [95% Cred. Interval]
-------------+----------------------------------------------------------------
cons | -.010594 .040224 189 0.395 -.0908355 .0678323
standlrt | .5568075 .0200344 839 0.000 .5167302 .5960792
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Mean Std. Dev. ESS [95% Cred. Int]
-----------------------------+------------------------------------------------
Level 2: school |
var(cons) | .0961163 .0199991 2715 .0644542 .1421674
cov(cons,standlrt) | .0190683 .0072205 1713 .0065699 .034989
var(standlrt) | .0153485 .0046893 978 .0078805 .0261361
-----------------------------+------------------------------------------------
Level 1: student |
var(cons) | .5542075 .0123209 4645 .5309043 .578929
------------------------------------------------------------------------------
------------------------------------------------------------------------------
cons | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
standlrt | 1.266334 .4572121 2.281147
------------------------------------------------------------------------------Example 2: Multivariate response model
(see page 214 of the MLwiN User Manual)
- read in data
use https://www.bristol.ac.uk/cmm/media/runmlwin/gcsemv1, clear- fit model using MLwiN via
runmlwin
runmlwin (written cons female, eq(1)) (csework cons female, eq(2)), ///
level1(student: (cons, eq(1)) (cons, eq(2))) ///
level2(school: (cons, eq(1)) (cons, eq(2))) ///
batch
--- Begin MLwiN error log ---
MLN - Software for N-level analysis. Mon 20 Oct 2025 08:54:50
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000005
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000009
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.00000a
--- End MLwiN error log ---
Warning: getversion plugin could not be loaded
MLwiN 3.16 multilevel model Number of obs = 1905
Multivariate response model (hierarchical)
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
school | 73 2 26.1 104
-----------------------------------------------------------
Run time (seconds) = 0.12
Number of iterations = 4
Log likelihood = -13400.244
Deviance = 26800.488
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
written |
cons_1 | 49.45213 .9338433 52.96 0.000 47.62183 51.28243
female_1 | -2.50295 .5607219 -4.46 0.000 -3.601945 -1.403955
-------------+----------------------------------------------------------------
csework |
cons_2 | 69.67166 1.171786 59.46 0.000 67.375 71.96831
female_2 | 6.751393 .6706493 10.07 0.000 5.436944 8.065841
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: school |
var(cons_1) | 46.81298 9.18733 28.80615 64.81982
cov(cons_1,cons_2) | 24.87783 8.880358 7.47265 42.28301
var(cons_2) | 75.16623 14.56485 46.61965 103.7128
-----------------------------+------------------------------------------------
Level 1: student |
var(cons_1) | 124.6343 4.349834 116.1088 133.1598
cov(cons_1,cons_2) | 73.00323 4.17829 64.81393 81.19252
var(cons_2) | 180.0982 6.245801 167.8566 192.3397
------------------------------------------------------------------------------/nogui option ignored
ECHO 0
Execution completed- report coefficient as median with 2.5 and 97.5 percentiles
reffadjustsim cons_1 cons_2, eqn(RP2) seed(12345)
------------------------------------------------------------------------------
cons_1 | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
cons_2 | .3308853 .1231026 .5395364
------------------------------------------------------------------------------- report coefficient as mean with Wald-type confidence interval
reffadjustsim cons_1 cons_2, eqn(RP2) seed(12345) waldtype
------------------------------------------------------------------------------
cons_1 | Coef. Std. Err. [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons_2 | .330939 .1065639 .1220777 .5398003
------------------------------------------------------------------------------
Warning: Coef. & Wald-type conf. interval limits may be inaccurate.
Please compare with default output which reports median & centiles.- compare with delta-method confidence interval (first refit model)
runmlwin (written cons female, eq(1)) (csework cons female, eq(2)), ///
level1(student: (cons, eq(1)) (cons, eq(2))) ///
level2(school: (cons, eq(1)) (cons, eq(2))) ///
batch
reffadjust4nlcom cons_1 cons_2, eqn(RP2)
nlcom `r(beta_cons_2)'
--- Begin MLwiN error log ---
MLN - Software for N-level analysis. Mon 20 Oct 2025 08:54:50
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.000006
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.00000a
/var/folders/1l/w0qjrymd3hb44s3skrzn4w4w0000gn/T//St94086.00000b
--- End MLwiN error log ---
Warning: getversion plugin could not be loaded
MLwiN 3.16 multilevel model Number of obs = 1905
Multivariate response model (hierarchical)
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
school | 73 2 26.1 104
-----------------------------------------------------------
Run time (seconds) = 0.10
Number of iterations = 4
Log likelihood = -13400.244
Deviance = 26800.488
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
written |
cons_1 | 49.45213 .9338433 52.96 0.000 47.62183 51.28243
female_1 | -2.50295 .5607219 -4.46 0.000 -3.601945 -1.403955
-------------+----------------------------------------------------------------
csework |
cons_2 | 69.67166 1.171786 59.46 0.000 67.375 71.96831
female_2 | 6.751393 .6706493 10.07 0.000 5.436944 8.065841
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: school |
var(cons_1) | 46.81298 9.18733 28.80615 64.81982
cov(cons_1,cons_2) | 24.87783 8.880358 7.47265 42.28301
var(cons_2) | 75.16623 14.56485 46.61965 103.7128
-----------------------------+------------------------------------------------
Level 1: student |
var(cons_1) | 124.6343 4.349834 116.1088 133.1598
cov(cons_1,cons_2) | 73.00323 4.17829 64.81393 81.19252
var(cons_2) | 180.0982 6.245801 167.8566 192.3397
------------------------------------------------------------------------------
_nl_1: [RP2]cov(cons_1\cons_2)/[RP2]var(cons_2)
------------------------------------------------------------------------------
| Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
_nl_1 | .3309709 .0986417 3.36 0.001 .1376367 .5243051
------------------------------------------------------------------------------/nogui option ignored
ECHO 0
Execution completed- compare with Bayesian posterior distribution
runmlwin (written cons female, eq(1)) (csework cons female, eq(2)), /// level1(student: (cons, eq(1)) (cons, eq(2))) /// level2(school: (cons, eq(1)) (cons, eq(2))) /// batch mcmc(on) initsprevious seed(121211) mcmcsum, getchains reffadjustsim cons_1 cons_2, eqn(RP2) mcmcsum
## Example 3: based on xtmixed helpfile
::: {#915cc68e .cell execution_count=13}
``` {.stata .cell-code}
webuse nlswork, clear
version 12: xtmixed ln_w grade age c.age#c.age ttl_exp tenure c.tenure#c.tenure || ///
idcode: tenure, cov(uns) var
version 12: reffadjustsim _cons tenure, eqn(idcode) seed(12345)(National Longitudinal Survey of Young Women, 14-24 years old in 1968)
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: Log likelihood = -8985.3816
Iteration 1: Log likelihood = -8966.2878
Iteration 2: Log likelihood = -8965.819
Iteration 3: Log likelihood = -8965.819
Computing standard errors:
Mixed-effects ML regression Number of obs = 28,099
Group variable: idcode Number of groups = 4,697
Obs per group:
min = 1
avg = 6.0
max = 15
Wald chi2(6) = 6767.13
Log likelihood = -8965.819 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
ln_wage | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
grade | .0690318 .0017885 38.60 0.000 .0655264 .0725372
age | .0321872 .0027908 11.53 0.000 .0267174 .037657
|
c.age#c.age | -.0006574 .0000466 -14.09 0.000 -.0007488 -.000566
|
ttl_exp | .0350762 .0011352 30.90 0.000 .0328513 .037301
tenure | .0393576 .0017198 22.88 0.000 .0359868 .0427284
|
c.tenure#|
c.tenure | -.0019926 .0001232 -16.18 0.000 -.0022341 -.0017512
|
_cons | .162264 .044888 3.61 0.000 .0742851 .2502429
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
idcode: Unstructured |
var(tenure) | .0005174 .0000566 .0004176 .0006411
var(_cons) | .0632497 .0020808 .0593001 .0674624
cov(tenure,_cons) | .0007165 .0002809 .0001658 .0012671
-----------------------------+------------------------------------------------
var(Residual) | .0815016 .0007978 .0799529 .0830803
------------------------------------------------------------------------------
LR test vs. linear model: chi2(3) = 9168.36 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
------------------------------------------------------------------------------
_cons | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
tenure | 1.377053 .2240176 2.619103
------------------------------------------------------------------------------:::
mixed ln_w grade age c.age#c.age ttl_exp tenure c.tenure#c.tenure || ///
idcode: tenure, cov(uns)
reffadjustsim _cons tenure, eqn(idcode) seed(12345)
Performing EM optimization ...
Performing gradient-based optimization:
Iteration 0: Log likelihood = -8985.3816
Iteration 1: Log likelihood = -8966.3961
Iteration 2: Log likelihood = -8965.819
Iteration 3: Log likelihood = -8965.819
Computing standard errors ...
Mixed-effects ML regression Number of obs = 28,099
Group variable: idcode Number of groups = 4,697
Obs per group:
min = 1
avg = 6.0
max = 15
Wald chi2(6) = 6767.13
Log likelihood = -8965.819 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
ln_wage | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
grade | .0690318 .0017885 38.60 0.000 .0655264 .0725372
age | .0321872 .0027908 11.53 0.000 .0267174 .037657
|
c.age#c.age | -.0006574 .0000466 -14.09 0.000 -.0007488 -.000566
|
ttl_exp | .0350762 .0011352 30.90 0.000 .0328513 .037301
tenure | .0393576 .0017198 22.88 0.000 .0359868 .0427284
|
c.tenure#|
c.tenure | -.0019926 .0001232 -16.18 0.000 -.0022341 -.0017512
|
_cons | .162264 .044888 3.61 0.000 .0742851 .2502429
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
idcode: Unstructured |
var(tenure) | .0005174 .0000566 .0004176 .0006411
var(_cons) | .0632497 .0020808 .0593001 .0674624
cov(tenure,_cons) | .0007165 .0002809 .0001658 .0012671
-----------------------------+------------------------------------------------
var(Residual) | .0815016 .0007978 .0799529 .0830803
------------------------------------------------------------------------------
LR test vs. linear model: chi2(3) = 9168.36 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
------------------------------------------------------------------------------
_cons | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
tenure | 1.377053 .224019 2.619103
------------------------------------------------------------------------------Example 4: based on xtmelogit helpfile
webuse bangladesh, clear
version 12: xtmelogit c_use urban age child* || district: urban , ///
cov(uns) var
version 12: reffadjustsim _cons urban, eqn(district) seed(12345)(Bangladesh Fertility Survey, 1989)
note: children omitted because of collinearity.
Refining starting values:
Iteration 0: Log likelihood = -1215.8594 (not concave)
Iteration 1: Log likelihood = -1204.0802
Iteration 2: Log likelihood = -1199.7968
Performing gradient-based optimization:
Iteration 0: Log likelihood = -1199.7968
Iteration 1: Log likelihood = -1199.4726
Iteration 2: Log likelihood = -1199.3158
Iteration 3: Log likelihood = -1199.315
Iteration 4: Log likelihood = -1199.315
Mixed-effects logistic regression Number of obs = 1,934
Group variable: district Number of groups = 60
Obs per group:
min = 2
avg = 32.2
max = 118
Integration points = 7 Wald chi2(5) = 97.50
Log likelihood = -1199.315 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
c_use | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
urban | .8157872 .1715519 4.76 0.000 .4795516 1.152023
age | -.026415 .008023 -3.29 0.001 -.0421398 -.0106902
child1 | 1.13252 .1603285 7.06 0.000 .818282 1.446758
child2 | 1.357739 .1770522 7.67 0.000 1.010724 1.704755
child3 | 1.353827 .1828801 7.40 0.000 .9953882 1.712265
children | 0 (omitted)
_cons | -1.71165 .1605617 -10.66 0.000 -2.026345 -1.396954
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
district: Unstructured |
var(urban) | .6663221 .3224714 .2580709 1.7204
var(_cons) | .3897434 .1292458 .2034723 .7465387
cov(urban,_cons) | -.4058846 .1755418 -.7499402 -.0618289
------------------------------------------------------------------------------
LR test vs. logistic model: chi2(3) = 58.42 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
------------------------------------------------------------------------------
_cons | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
urban | -.5931825 -.9623632 -.2892334
------------------------------------------------------------------------------webuse bangladesh, clear
meqrlogit c_use urban age child* || district: urban, cov(uns)
reffadjustsim _cons urban, eqn(district) seed(12345)(Bangladesh Fertility Survey, 1989)
note: children omitted because of collinearity.
Refining starting values:
Iteration 0: Log likelihood = -1215.8594 (not concave)
Iteration 1: Log likelihood = -1204.0802
Iteration 2: Log likelihood = -1199.7968
Performing gradient-based optimization:
Iteration 0: Log likelihood = -1199.7968
Iteration 1: Log likelihood = -1199.4726
Iteration 2: Log likelihood = -1199.3158
Iteration 3: Log likelihood = -1199.315
Iteration 4: Log likelihood = -1199.315
Mixed-effects logistic regression Number of obs = 1,934
Group variable: district Number of groups = 60
Obs per group:
min = 2
avg = 32.2
max = 118
Integration points = 7 Wald chi2(5) = 97.50
Log likelihood = -1199.315 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
c_use | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
urban | .8157872 .1715519 4.76 0.000 .4795516 1.152023
age | -.026415 .008023 -3.29 0.001 -.0421398 -.0106902
child1 | 1.13252 .1603285 7.06 0.000 .818282 1.446758
child2 | 1.357739 .1770522 7.67 0.000 1.010724 1.704755
child3 | 1.353827 .1828801 7.40 0.000 .9953882 1.712265
children | 0 (omitted)
_cons | -1.71165 .1605617 -10.66 0.000 -2.026345 -1.396954
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
district: Unstructured |
var(urban) | .6663221 .3224714 .2580709 1.7204
var(_cons) | .3897434 .1292458 .2034723 .7465387
cov(urban,_cons) | -.4058846 .1755418 -.7499402 -.0618289
------------------------------------------------------------------------------
LR test vs. logistic model: chi2(3) = 58.42 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
------------------------------------------------------------------------------
_cons | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
urban | -.5931825 -.9623632 -.2892334
------------------------------------------------------------------------------Example 5: based on xtmepoisson helpfile
webuse epilepsy, clear
version 12: xtmepoisson seizures treat lbas lbas_trt lage visit || ///
subject: visit, cov(uns) var intpoints(9)
version 12: reffadjustsim _cons visit, eqn(subject) seed(12345)(Epilepsy data; progabide drug treatment)
Refining starting values:
Iteration 0: Log likelihood = -672.17188 (not concave)
Iteration 1: Log likelihood = -660.46056
Iteration 2: Log likelihood = -655.86888
Performing gradient-based optimization:
Iteration 0: Log likelihood = -655.86888
Iteration 1: Log likelihood = -655.68217
Iteration 2: Log likelihood = -655.68103
Iteration 3: Log likelihood = -655.68103
Mixed-effects Poisson regression Number of obs = 236
Group variable: subject Number of groups = 59
Obs per group:
min = 4
avg = 4.0
max = 4
Integration points = 9 Wald chi2(5) = 115.56
Log likelihood = -655.68103 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
seizures | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
treat | -.9286588 .4021638 -2.31 0.021 -1.716885 -.1404323
lbas | .8849767 .1312519 6.74 0.000 .6277277 1.142226
lbas_trt | .3379757 .2044442 1.65 0.098 -.0627276 .738679
lage | .4767192 .353622 1.35 0.178 -.2163672 1.169806
visit | -.2664098 .1647096 -1.62 0.106 -.5892347 .0564151
_cons | 2.099555 .2203709 9.53 0.000 1.667636 2.531474
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
subject: Unstructured |
var(visit) | .5314808 .2293851 .2280931 1.238406
var(_cons) | .2514928 .0587892 .1590552 .3976522
cov(visit,_cons) | .0028715 .0887018 -.1709808 .1767238
------------------------------------------------------------------------------
LR test vs. Poisson model: chi2(3) = 324.54 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
------------------------------------------------------------------------------
_cons | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
visit | .0044339 -.3128618 .3397986
------------------------------------------------------------------------------meqrpoisson seizures treat lbas lbas_trt lage visit || subject: visit, ///
cov(uns) intpoints(9)
reffadjustsim _cons visit, eqn(subject) seed(12345)
Refining starting values:
Iteration 0: Log likelihood = -672.17188 (not concave)
Iteration 1: Log likelihood = -660.46056
Iteration 2: Log likelihood = -655.86888
Performing gradient-based optimization:
Iteration 0: Log likelihood = -655.86888
Iteration 1: Log likelihood = -655.68217
Iteration 2: Log likelihood = -655.68103
Iteration 3: Log likelihood = -655.68103
Mixed-effects Poisson regression Number of obs = 236
Group variable: subject Number of groups = 59
Obs per group:
min = 4
avg = 4.0
max = 4
Integration points = 9 Wald chi2(5) = 115.56
Log likelihood = -655.68103 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
seizures | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
treat | -.9286588 .4021638 -2.31 0.021 -1.716885 -.1404323
lbas | .8849767 .1312519 6.74 0.000 .6277277 1.142226
lbas_trt | .3379757 .2044442 1.65 0.098 -.0627276 .738679
lage | .4767192 .353622 1.35 0.178 -.2163672 1.169806
visit | -.2664098 .1647096 -1.62 0.106 -.5892347 .0564151
_cons | 2.099555 .2203709 9.53 0.000 1.667636 2.531474
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
subject: Unstructured |
var(visit) | .5314808 .2293851 .2280931 1.238406
var(_cons) | .2514928 .0587892 .1590552 .3976522
cov(visit,_cons) | .0028715 .0887018 -.1709808 .1767238
------------------------------------------------------------------------------
LR test vs. Poisson model: chi2(3) = 324.54 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
------------------------------------------------------------------------------
_cons | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
visit | .0044339 -.3128618 .3397986
------------------------------------------------------------------------------Example 6: repeated group variable
webuse nlswork, clear
version 12: xtmixed ln_w grade age || idcode: tenure union, cov(uns) || ///
idcode: race, cov(uns) var
version 12: reffadjustsim tenure union, eqn(idcode) sub(1) seed(12345)
version 12: reffadjustsim race _cons, eqn(idcode) sub(2) seed(12345)(National Longitudinal Survey of Young Women, 14-24 years old in 1968)
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: Log likelihood = -5208.9293
Iteration 1: Log likelihood = -5196.8989 (not concave)
Iteration 2: Log likelihood = -5176.1013
Iteration 3: Log likelihood = -5174.4196
Iteration 4: Log likelihood = -5171.2001
Iteration 5: Log likelihood = -5171.0595
Iteration 6: Log likelihood = -5171.0493
Iteration 7: Log likelihood = -5171.0492
Computing standard errors:
Mixed-effects ML regression Number of obs = 19,008
Group variable: idcode Number of groups = 4,132
Obs per group:
min = 1
avg = 4.6
max = 12
Wald chi2(2) = 2164.18
Log likelihood = -5171.0492 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
ln_wage | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
grade | .083625 .0022067 37.90 0.000 .0793 .08795
age | .0104518 .0004215 24.80 0.000 .0096256 .0112779
_cons | .2739857 .0306868 8.93 0.000 .2138407 .3341308
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
idcode: Unstructured |
var(tenure) | .0007758 .0000666 .0006556 .0009181
var(union) | .0514475 .0041886 .0438594 .0603483
cov(tenure,union) | .0016667 .0004909 .0007044 .0026289
-----------------------------+------------------------------------------------
idcode: Unstructured |
var(race) | .0202217 .0139137 .0052498 .0778921
var(_cons) | .1578058 .031278 .1070066 .2327211
cov(race,_cons) | -.0401861 .022046 -.0833954 .0030232
-----------------------------+------------------------------------------------
var(Residual) | .0583641 .0007602 .0568929 .0598732
------------------------------------------------------------------------------
LR test vs. linear model: chi2(6) = 10154.21 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
------------------------------------------------------------------------------
tenure | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
union | .0321253 .0121897 .0521846
------------------------------------------------------------------------------
------------------------------------------------------------------------------
race | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
_cons | -.2544331 -.476558 -.1166022
------------------------------------------------------------------------------mixed ln_w grade age || idcode: tenure union, cov(uns) || idcode: race, ///
cov(uns)
reffadjustsim tenure union, eqn(idcode) sub(1) seed(12345)
reffadjustsim race _cons, eqn(idcode) sub(2) seed(12345)
Performing EM optimization ...
Performing gradient-based optimization:
Iteration 0: Log likelihood = -5208.9293
Iteration 1: Log likelihood = -5199.2605 (not concave)
Iteration 2: Log likelihood = -5175.53 (not concave)
Iteration 3: Log likelihood = -5173.8245
Iteration 4: Log likelihood = -5171.2707
Iteration 5: Log likelihood = -5171.0525
Iteration 6: Log likelihood = -5171.0492
Iteration 7: Log likelihood = -5171.0492
Computing standard errors ...
Mixed-effects ML regression Number of obs = 19,008
Group variable: idcode Number of groups = 4,132
Obs per group:
min = 1
avg = 4.6
max = 12
Wald chi2(2) = 2164.18
Log likelihood = -5171.0492 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
ln_wage | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
grade | .083625 .0022067 37.90 0.000 .0793 .08795
age | .0104518 .0004215 24.80 0.000 .0096256 .0112779
_cons | .2739857 .0306868 8.93 0.000 .2138407 .3341307
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects parameters | Estimate Std. err. [95% conf. interval]
-----------------------------+------------------------------------------------
idcode: Unstructured |
var(tenure) | .0007758 .0000666 .0006556 .0009181
var(union) | .0514475 .0041886 .0438595 .0603483
cov(tenure,union) | .0016667 .0004909 .0007044 .0026289
-----------------------------+------------------------------------------------
idcode: Unstructured |
var(race) | .0202217 .0139134 .0052499 .07789
var(_cons) | .1578059 .0312773 .1070075 .2327191
cov(race,_cons) | -.0401862 .0220455 -.0833945 .0030222
-----------------------------+------------------------------------------------
var(Residual) | .0583641 .0007602 .0568929 .0598732
------------------------------------------------------------------------------
LR test vs. linear model: chi2(6) = 10154.21 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference.
------------------------------------------------------------------------------
tenure | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
union | .0321253 .0121897 .0521846
------------------------------------------------------------------------------
------------------------------------------------------------------------------
race | Median 2.5 Percentile 97.5 Percentile
-------------+----------------------------------------------------------------
_cons | -.2544335 -.4765523 -.1166046
------------------------------------------------------------------------------