Model Averaging with Covariates

First of all, let me make sure I understand correctly. If I run covariate.predictions on a marklist with models that have covariates, it will compute the model averaged estimates at the mean of the covariate values (unless I specify covariate values), right?
This is what I did, but I am having trouble because I have an unusual dummy (design) covariate structure that produces a lot of singular betas. (It produces the same real estimates as the model in PRESENCE I am trying to duplicate however.) I tried to run model-averaging with drop=FALSE
and it seems to work for the estimates and SE's but the CI's have 0 width for parameter 2-5:
Parm Index estimate SE LCL UCL
1 0.730372229 0.073582331 0.565658267 0.849267234
2 1.08E-05 4.24E-05 1.08E-05 1.08E-05
3 0.003440508 0.006970964 0.003440508 0.003440508
4 0.626230224 0.339959521 0.626230215 0.626230225
5 0.358793485 0.183570311 0.358793487 0.358793485
6 0.708071 0.046150174 0.610267243 0.789785307
7 0.714957549 0.045729741 0.617694767 0.795661347
Parameters 2-3 are the at the boundary, but I've looked at the models with the most weight for parameters 4-5 and they have good CIs. So I'm not sure why the model averaging isn't working.
Is there a way to get this to work with all my singular betas?
It may be possible to construct a covariate structure with fewer singular betas, but there are always going to be some.
This is what I did, but I am having trouble because I have an unusual dummy (design) covariate structure that produces a lot of singular betas. (It produces the same real estimates as the model in PRESENCE I am trying to duplicate however.) I tried to run model-averaging with drop=FALSE
- Code: Select all
Model.ests.4.all=covariate.predictions(LIBL.models4, data=cov1.df, drop = FALSE)
and it seems to work for the estimates and SE's but the CI's have 0 width for parameter 2-5:
Parm Index estimate SE LCL UCL
1 0.730372229 0.073582331 0.565658267 0.849267234
2 1.08E-05 4.24E-05 1.08E-05 1.08E-05
3 0.003440508 0.006970964 0.003440508 0.003440508
4 0.626230224 0.339959521 0.626230215 0.626230225
5 0.358793485 0.183570311 0.358793487 0.358793485
6 0.708071 0.046150174 0.610267243 0.789785307
7 0.714957549 0.045729741 0.617694767 0.795661347
Parameters 2-3 are the at the boundary, but I've looked at the models with the most weight for parameters 4-5 and they have good CIs. So I'm not sure why the model averaging isn't working.
Is there a way to get this to work with all my singular betas?
It may be possible to construct a covariate structure with fewer singular betas, but there are always going to be some.