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- Equivalent of 'Restrict' command in Proc Mixed?

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06-12-2011 02:24 PM

Hi,

I'm running a 3 level model in proc mixed and need to set the parameter estimates of my three level 2 predictors equal to each other. I know there is a 'restrict' command in proc reg that will do this. Is there an equivalent command in proc mixed?

Thanks! Message was edited by: sarph

I'm running a 3 level model in proc mixed and need to set the parameter estimates of my three level 2 predictors equal to each other. I know there is a 'restrict' command in proc reg that will do this. Is there an equivalent command in proc mixed?

Thanks! Message was edited by: sarph

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06-13-2011 10:06 PM

There is no explicit restrict command. Sometimes an explicit restriction can be re-formulated as an implicit restriction. For instance, with x1 and x2, and the same parameter for each, one could write:

y = beta0 + beta*x1 + beta*x2

as

y = beta0 + beta*(x1+x2).

So, if you define xsum = x1+x2 in a data step, you could then use xsum as a single covariable in mixed. Of course, other restrictions are not so easy to formulate. Edwards and colleagues have done some good work on writing macros to allow for more arbitrary linear constraints on the parameters. Basically, "new" variables are constructed outside of mixed based on the constraints chosen, and then mixed is used to fit the model (with follow-up re-formulation). If you are statistically savvy, you might enjoy the article in Biometrics, vol. 57, pp. 1185-1190 (2001). I have not tried to use the the code made available by the authors. There may be other contributions in the last few years.

y = beta0 + beta*x1 + beta*x2

as

y = beta0 + beta*(x1+x2).

So, if you define xsum = x1+x2 in a data step, you could then use xsum as a single covariable in mixed. Of course, other restrictions are not so easy to formulate. Edwards and colleagues have done some good work on writing macros to allow for more arbitrary linear constraints on the parameters. Basically, "new" variables are constructed outside of mixed based on the constraints chosen, and then mixed is used to fit the model (with follow-up re-formulation). If you are statistically savvy, you might enjoy the article in Biometrics, vol. 57, pp. 1185-1190 (2001). I have not tried to use the the code made available by the authors. There may be other contributions in the last few years.