[R] Combining estimates from multiple regressions

[James Shaw]

Thanks for the suggestions, Gunter.



On Wed, Jun 24, 2015 at 10:33 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote:
> Not an answer to your question, but you should not be using "dummy"
> variables in R. Use factors instead. Please read a R tutorial or text
> -- there are many -- to learn how to fit models in R. You might also
> wish to consult a local statistician or post on a statistics list like
> stats.stackexchange.com for statistics questions, which are off topic
> here.
>
> Further, when you post here, please read and follow the posting guide
> (below) and post in plain text, not HTML.
>
> Cheers,
> Bert
> Bert Gunter
>
> "Data is not information. Information is not knowledge. And knowledge
> is certainly not wisdom."
>    -- Clifford Stoll
>
>
> On Wed, Jun 24, 2015 at 3:27 AM, James Shaw <shawjw at gmail.com> wrote:
>> I am interested in using quantile regression to fit the following model at
>> different quantiles of a response variable:
>>
>> (1)  y = b0 + b1*g1 + b2*g2 + B*Z
>>
>> where b0 is an intercept, g1 and g2 are dummy variables for 2 of 3
>> independent groups, and Z is a matrix of covariates to be adjusted for in
>> the estimation (e.g., age, gender).  The problem is that estimates for g2
>> and g1 are not estimable at all quantiles.  To overcome this, one option is
>> to fit a separate model for each group (i.e., group 0, which is reflected
>> by intercept above, group 1, and group 2):
>>
>> (2)  y = b11 + B1*Z (model for group 0)
>> (3)  y = b12 + B2*Z (model for group 1)
>> (4)  y = b13 + B3*Z (model for group 2)
>>
>> This would correspond to fitting a single model in which group membership
>> was interacted with all covariates, albeit some of the interaction terms
>> would not be estimable for the reason noted above.  However, I ultimately
>> would like to base inferences on a single set of estimates.
>>
>> Can anyone suggest an approach to combine estimates from models (2)-(4),
>> perhaps through weighted averaging, to generate estimates for the model
>> presented in (1) above?  An approach is not immediately clear to me since
>> the group effects are subsumed in the intercepts in (2)-(4), whereas (1)
>> includes separate estimates of group effects instead of a single weighted
>> average.
>>
>> Regards,
>>
>> Jim
>>
>>         [[alternative HTML version deleted]]
>>
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