Welcome to the forum for runmlwin users. Feel free to post your question about runmlwin here. The Centre for Multilevel Modelling take no responsibility for the accuracy of these posts, we are unable to monitor them closely. Do go ahead and post your question and thank you in advance if you find the time to post any answers!
Go to runmlwin: Running MLwiN from within Stata >>
http://www.bristol.ac.uk/cmm/software/runmlwin/
erikruzek
Posts: 16 Joined: Tue May 21, 2019 3:57 pm
Post
by erikruzek Wed May 29, 2019 10:50 pm
Hello,
I am trying out the code provided for runmlwin to replicate the analyses in the MLwiN Users Manual (thank you for doing that, Chris and George!) and I have a question about some of the code for cross-classified models in Chapter 18.
To run the model in 18.4, the runmlwin .do file has the following code that comes after you create the school dummy variables:
Code: Select all
local c = 1
forvalues s=2/19 {
constraint define `c' [RP3]var(s1) = [RP3]var(s`s')
local c = `c' + 1
}
And then it has the syntax to run the model using runmlwin.
However, if I do not run that bit of code and run the model, the results are exactly the same. Is this code needed? If so, what is it doing?
Thanks,
Erik
ChrisCharlton
Posts: 1384 Joined: Mon Oct 19, 2009 10:34 am
Post
by ChrisCharlton Thu May 30, 2019 9:22 am
These lines define the constraint that the level-3 variances are all equal and correspond to the numbers in the constraints option passed to runmlwin:
If you run the model without these then these then the variances will be estimated as different:
Code: Select all
. runmlwin attain cons, ///
> level3(cons: s1-s19, diagonal) ///
> level2(pid: cons) level1(pupil: cons) ///
> nopause
MLwiN 3.03 multilevel model Number of obs = 3435
Normal response model
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
cons | 1 3435 3435.0 3435
pid | 148 1 23.2 72
-----------------------------------------------------------
Run time (seconds) = 1.73
Number of iterations = 7
Log likelihood = -8566.0265
Deviance = 17132.053
------------------------------------------------------------------------------
attain | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | 5.709704 .1181172 48.34 0.000 5.478199 5.94121
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 3: cons |
var(s1) | 0 0 0 0
var(s2) | 0 0 0 0
var(s3) | .5790943 1.032502 -1.444572 2.602761
var(s4) | 0 0 0 0
var(s5) | 0 0 0 0
var(s6) | .1585827 .396016 -.6175945 .9347599
var(s7) | 0 0 0 0
var(s8) | 1.62833 2.651101 -3.567731 6.824392
var(s9) | .7782774 1.379918 -1.926312 3.482867
var(s10) | .132258 .4859599 -.8202059 1.084722
var(s11) | 0 0 0 0
var(s12) | .6527393 1.072904 -1.450115 2.755593
var(s13) | 0 0 0 0
var(s14) | 0 0 0 0
var(s15) | 0 0 0 0
var(s16) | 0 0 0 0
var(s17) | .1225453 .3672506 -.5972527 .8423433
var(s18) | 0 0 0 0
var(s19) | 4.30737 6.29295 -8.026587 16.64133
-----------------------------+------------------------------------------------
Level 2: pid |
var(cons) | 1.10217 .1892235 .7312989 1.473041
-----------------------------+------------------------------------------------
Level 1: pupil |
var(cons) | 8.099678 .1994997 7.708665 8.49069
------------------------------------------------------------------------------
whereas including them sets the variances to be equal:
Code: Select all
. runmlwin attain cons, ///
> level3(cons: s1-s19, diagonal) ///
> level2(pid: cons) level1(pupil: cons) ///
> constraints(1/18) nopause
( 1) [RP3]var(s1) - [RP3]var(s2) = 0
( 2) [RP3]var(s1) - [RP3]var(s3) = 0
( 3) [RP3]var(s1) - [RP3]var(s4) = 0
( 4) [RP3]var(s1) - [RP3]var(s5) = 0
( 5) [RP3]var(s1) - [RP3]var(s6) = 0
( 6) [RP3]var(s1) - [RP3]var(s7) = 0
( 7) [RP3]var(s1) - [RP3]var(s8) = 0
( 8) [RP3]var(s1) - [RP3]var(s9) = 0
( 9) [RP3]var(s1) - [RP3]var(s10) = 0
(10) [RP3]var(s1) - [RP3]var(s11) = 0
(11) [RP3]var(s1) - [RP3]var(s12) = 0
(12) [RP3]var(s1) - [RP3]var(s13) = 0
(13) [RP3]var(s1) - [RP3]var(s14) = 0
(14) [RP3]var(s1) - [RP3]var(s15) = 0
(15) [RP3]var(s1) - [RP3]var(s16) = 0
(16) [RP3]var(s1) - [RP3]var(s17) = 0
(17) [RP3]var(s1) - [RP3]var(s18) = 0
(18) [RP3]var(s1) - [RP3]var(s19) = 0
MLwiN 3.03 multilevel model Number of obs = 3435
Normal response model
Estimation algorithm: IGLS
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
cons | 1 3435 3435.0 3435
pid | 148 1 23.2 72
-----------------------------------------------------------
Run time (seconds) = 1.75
Number of iterations = 4
Log likelihood = -8574.5656
Deviance = 17149.131
------------------------------------------------------------------------------
attain | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | 5.504051 .1748762 31.47 0.000 5.161299 5.846802
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 3: cons |
var(s1) | .347889 .1622717 .0298423 .6659356
var(s2) | .347889 .1622717 .0298423 .6659356
var(s3) | .347889 .1622717 .0298423 .6659356
var(s4) | .347889 .1622717 .0298423 .6659356
var(s5) | .347889 .1622717 .0298423 .6659356
var(s6) | .347889 .1622717 .0298423 .6659356
var(s7) | .347889 .1622717 .0298423 .6659356
var(s8) | .347889 .1622717 .0298423 .6659356
var(s9) | .347889 .1622717 .0298423 .6659356
var(s10) | .347889 .1622717 .0298423 .6659356
var(s11) | .347889 .1622717 .0298423 .6659356
var(s12) | .347889 .1622717 .0298423 .6659356
var(s13) | .347889 .1622717 .0298423 .6659356
var(s14) | .347889 .1622717 .0298423 .6659356
var(s15) | .347889 .1622717 .0298423 .6659356
var(s16) | .347889 .1622717 .0298423 .6659356
var(s17) | .347889 .1622717 .0298423 .6659356
var(s18) | .347889 .1622717 .0298423 .6659356
var(s19) | .347889 .1622717 .0298423 .6659356
-----------------------------+------------------------------------------------
Level 2: pid |
var(cons) | 1.123741 .1979374 .7357908 1.511691
-----------------------------+------------------------------------------------
Level 1: pupil |
var(cons) | 8.111626 .1999722 7.719688 8.503565
------------------------------------------------------------------------------
I suspect that when you are running for the second time you haven't cleared the constraints from the original run or removed the constraints option from your command, so it is able to pick them up without you having to explicitly define them again.
erikruzek
Posts: 16 Joined: Tue May 21, 2019 3:57 pm
Post
by erikruzek Thu May 30, 2019 2:47 pm
Ah, I see. Yes, I did not clear the constraints when I re-ran the model after re-loading the data.