-------------------------------------------------------------------------------
      name:  <unnamed>
       log:  Q:\C-modelling\runmlwin\website\logfiles\2020-03-27\16\13.3.smcl
  log type:  smcl
 opened on:  27 Mar 2020, 18:24:17

. ****************************************************************************
. * Module 13: Multiple Membership Models - Stata Practical
. *
. *     P13.3: Exploring Alternative Multiple Membership Weighting Schemes
. *
. *            George Leckie
. *            Centre for Multilevel Modelling, 2011
. ****************************************************************************
. *     Stata do-file to replicate all analyses using runmlwin
. *
. *     George Leckie
. *     Centre for Multilevel Modelling, 2013
. *     http://www.bristol.ac.uk/cmm/software/runmlwin/
. ****************************************************************************
. 
. use "http://www.bristol.ac.uk/cmm/media/runmlwin/13.3.dta", clear

. 
. forvalues j = 1/25 {
  2.         replace p`j' = 1/nurses if p`j'~=0
  3. }
(72 real changes made)
(69 real changes made)
(64 real changes made)
(56 real changes made)
(60 real changes made)
(71 real changes made)
(65 real changes made)
(61 real changes made)
(53 real changes made)
(53 real changes made)
(56 real changes made)
(77 real changes made)
(75 real changes made)
(76 real changes made)
(58 real changes made)
(61 real changes made)
(59 real changes made)
(58 real changes made)
(62 real changes made)
(50 real changes made)
(74 real changes made)
(56 real changes made)
(63 real changes made)
(60 real changes made)
(60 real changes made)

. 
. xtmixed satis || _all: p1-p25, nocons covariance(identity) mle variance

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -1349.0992  
Iteration 1:   log likelihood = -1349.0992  

Computing standard errors:

Mixed-effects ML regression                     Number of obs     =      1,000
Group variable: _all                            Number of groups  =          1

                                                Obs per group:
                                                              min =      1,000
                                                              avg =    1,000.0
                                                              max =      1,000

                                                Wald chi2(0)      =          .
Log likelihood = -1349.0992                     Prob > chi2       =          .

------------------------------------------------------------------------------
       satis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0250507   .0974989    -0.26   0.797    -.2161451    .1660437
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
_all: Identity               |
             var(p1..p25)(1) |   .2165267   .0708471       .114025    .4111716
-----------------------------+------------------------------------------------
               var(Residual) |   .8266394   .0374413      .7564187    .9033789
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 100.26        Prob >= chibar2 = 0.0000
(1) p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 p11 p12 p13 p14 p15 p16 p17 p18 p19 p20
    p21 p22 p23 p24 p25

. 
. estimates store model2

. 
. xtmixed satis || n1st:, mle variance

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -1374.3654  
Iteration 1:   log likelihood = -1374.3654  

Computing standard errors:

Mixed-effects ML regression                     Number of obs     =      1,000
Group variable: n1st                            Number of groups  =         25

                                                Obs per group:
                                                              min =         25
                                                              avg =       40.0
                                                              max =         56

                                                Wald chi2(0)      =          .
Log likelihood = -1374.3654                     Prob > chi2       =          .

------------------------------------------------------------------------------
       satis |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       _cons |  -.0297641   .0653463    -0.46   0.649    -.1578405    .0983122
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
n1st: Identity               |
                  var(_cons) |   .0841853   .0303888      .0414929    .1708043
-----------------------------+------------------------------------------------
               var(Residual) |   .8796282   .0398453      .8048993    .9612951
------------------------------------------------------------------------------
LR test vs. linear model: chibar2(01) = 49.73         Prob >= chibar2 = 0.0000

. 
. estimates store model3

. 
end of do-file