Error message: ...Constraint matrix is invalid...

[michaellawton]

Hello

I had a multilevel growth curve model programed in runmlwin on my desktop using MLwiN 2.23. The program included constraints such as:

constraint define 1 [RP2]var(cons) = 0.259
constraint define 2 ...

runmlwin ... constraint(1/4)

The program worked fine, however I am now trying to run the same program on a laptop using MLwiN 2.24 and I get the following error message

"Row 1 of the constraint matrix is invalid as it involves both fixed part and random part parameters"

Any help on how to fix this would be greatly appreciated. Many thanks, Michael

[GeorgeLeckie]

Hi Michael,

In the latest release of runmlwin we made runmlwin backwards compatible with Stata versions 9 and 10. However, a bug crept in relating to the constraints.

We have now fixed the bug for the next SSC release.

Michael, I will email you the fix. If anyone else would like the fix then please contact me by replying to this post.

Below is a rather contrived example where we constrain the the random part parameters of a linear growth curve model to be specific values. The example shows what the runmlwin output should look like after you have applied the fix.

Code: Select all

. use "http://www.stata-press.com/data/mlmus2/asian.dta", clear

. 
. gen cons = 1

. 
. gen age2 = age^2

. 
. runmlwin weight cons age age2, ///
>         level2(id: cons age) ///
>         level1(occ: cons) ///
>         nopause
 
MLwiN 2.24 multilevel model                     Number of obs      =       198
Normal response model
Estimation algorithm: IGLS

-----------------------------------------------------------
                |   No. of       Observations per Group
 Level Variable |   Groups    Minimum    Average    Maximum
----------------+------------------------------------------
             id |       68          1        2.9          5
-----------------------------------------------------------

Run time (seconds)   =       1.58
Number of iterations =          7
Log likelihood       = -258.07785
Deviance             =   516.1557
------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        cons |   3.494518   .1372489    25.46   0.000     3.225515     3.76352
         age |   7.704002   .2394275    32.18   0.000     7.234733    8.173271
        age2 |  -1.660475   .0885319   -18.76   0.000    -1.833994   -1.486955
------------------------------------------------------------------------------

------------------------------------------------------------------------------
   Random-effects Parameters |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: id                  |
                   var(cons) |   .4040045   .1412488      .1271619    .6808471
               cov(cons,age) |    .088273   .0812774     -.0710279    .2475738
                    var(age) |   .2539857   .0858503      .0857222    .4222493
-----------------------------+------------------------------------------------
Level 1: occ                 |
                   var(cons) |    .331641   .0532307      .2273107    .4359712
------------------------------------------------------------------------------

. 
. constraint define 1 [RP2]var(cons)      = 0.5   

. constraint define 2 [RP2]cov(cons\age)  = 0.1   

. constraint define 3 [RP2]var(age)       = 0.25  

. constraint define 4 [RP1]var(cons)      = 0.3   

.         
. runmlwin weight cons age age2, ///
>         level2(id: cons age) ///
>         level1(occ: cons) ///
>         constraints(1/4) nopause
 
 ( 1)  [RP2]var(cons) = .5
 ( 2)  [RP2]cov(cons\age) = .1
 ( 3)  [RP2]var(age) = .25
 ( 4)  [RP1]var(cons) = .3
MLwiN 2.24 multilevel model                     Number of obs      =       198
Normal response model
Estimation algorithm: IGLS

-----------------------------------------------------------
                |   No. of       Observations per Group
 Level Variable |   Groups    Minimum    Average    Maximum
----------------+------------------------------------------
             id |       68          1        2.9          5
-----------------------------------------------------------

Run time (seconds)   =       1.37
Number of iterations =          2
Log likelihood       = -258.49789
Deviance             =  516.99579
------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        cons |   3.489141   .1388873    25.12   0.000     3.216926    3.761355
         age |   7.713635   .2307907    33.42   0.000     7.261294    8.165977
        age2 |  -1.663078   .0850955   -19.54   0.000    -1.829862   -1.496294
------------------------------------------------------------------------------

------------------------------------------------------------------------------
   Random-effects Parameters |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: id                  |
                   var(cons) |         .5   6.32e-09            .5          .5
               cov(cons,age) |         .1   2.85e-09            .1          .1
                    var(age) |        .25   1.64e-09           .25         .25
-----------------------------+------------------------------------------------
Level 1: occ                 |
                   var(cons) |         .3   6.37e-09            .3          .3
------------------------------------------------------------------------------

Best wishes

George