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
Error message: ...Constraint matrix is invalid...
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Re: Error message: ...Constraint matrix is invalid...
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.
Best wishes
George
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