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Re: st: More mixed confusion
From
Lucas <[email protected]>
To
[email protected]
Subject
Re: st: More mixed confusion
Date
Wed, 10 Jul 2013 12:44:43 -0700
Thanks! But, is that constant a constant, a variance, something else
(e.g., a mean for the context-level error term)?
I guess my confusion is that when I think "equation" I think there's a
left hand side of some outcome (which could be a variance) and a right
hand side of one or more inputs. If we say there's an equation for
the variance, then is the constant a variance? I think you can see my
confusion just by juxtaposing "constant" and "variance". (Feels like
"Who's on first?" -- I can't believe this is the clearest way for
these statistical ideas to be expressed.)
Anyway, thanks a bunch for your response. So, now my question is, is
the constant a variance? Something else?
Sam
On Wed, Jul 10, 2013 at 12:33 PM, Scott Baldwin <[email protected]> wrote:
> Suppressing the constant in the random effects will ensure that a
> random intercept for the given id variable isn't estimated. The
> --nocons-- option is useful in a number of scenarios. For example,
> using the 'childweight' data from the manuals, you can fit the
> following model:
>
> webuse childweight
> mixed weight age || id:
>
> which provides the variance for the random intercept (person specific
> deflections in weight)
>
> ------------------------------------------------------------------------------
> Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
> -----------------------------+------------------------------------------------
> id: Identity |
> var(_cons) | .6076662 .2040674 .3146395 1.173591
> -----------------------------+------------------------------------------------
> var(Residual) | 1.524052 .1866496 1.198819 1.937518
> ------------------------------------------------------------------------------
>
> This dataset includes boys and girls.
>
> tabulate girl
>
> gender | Freq. Percent Cum.
> ------------+-----------------------------------
> boy | 100 50.51 50.51
> girl | 98 49.49 100.00
> ------------+-----------------------------------
> Total | 198 100.00
>
> we may want to estimate a unique random intercept for boys and one for
> girls because we think the person specific deflections in weight are
> different for boys and girls. To do this you need to create a boy
> dummy variable to go with the girl dummy variable (I don't think you
> can use standard factor variables in --mixed--).
>
> gen boy=girl==0
>
> tabulate girl, nolabel
> tabulate boy, nolabel
>
>
> The model is specified as:
>
> mixed weight age || id: boy girl, nocons
>
> which gives you separate random intercepts variances by boy and girl.
>
> ------------------------------------------------------------------------------
> Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
> -----------------------------+------------------------------------------------
> id: Independent |
> var(boy) | .7620931 .3329286 .3237065 1.794174
> var(girl) | .4330075 .2723947 .12619 1.485819
> -----------------------------+------------------------------------------------
> var(Residual) | 1.530808 .1887532 1.202168 1.94929
> ------------------------------------------------------------------------------
>
> You need to suppress the overall intercept in the random effects
> portion or you can't estimate this model (the dummy variables will be
> collinear with the overall intercept). Indeed, try
>
> mixed weight age || id: boy girl
>
> you will see that Stata will automatically drop one of the variables.
>
> Best,
> Scott
>
>
> On Wed, Jul 10, 2013 at 1:15 PM, Scott Baldwin <[email protected]> wrote:
>> Sorry about that -- I sent my email before I was done. Starting over...
>>
>> Suppressing the constant in the random effects will ensure that a random
>> intercept for the given id variable isn't estimated. The --nocons-- option
>> is useful in a number of scenarios. For example, using the 'childweight'
>> data from the manuals, you can fit the following model:
>>
>> webuse childweight
>> mixed weight age || id:
>>
>> which provides the variance for the random intercept (person specific
>> deflections in weight)
>>
>> ------------------------------------------------------------------------------
>> Random-effects Parameters | Estimate Std. Err. [95% Conf.
>> Interval]
>> -----------------------------+------------------------------------------------
>> id: Identity |
>> var(_cons) | .6076662 .2040674 .3146395
>> 1.173591
>> -----------------------------+------------------------------------------------
>> var(Residual) | 1.524052 .1866496 1.198819
>> 1.937518
>> ------------------------------------------------------------------------------
>>
>> This dataset includes boys and girls.
>>
>> tabulate girl
>>
>> gender | Freq. Percent Cum.
>> ------------+-----------------------------------
>> boy | 100 50.51 50.51
>> girl | 98 49.49 100.00
>> ------------+-----------------------------------
>> Total | 198 100.00
>>
>> we may want to estimate a unique random intercept for boys and one for girls
>> because we think the person specific deflections in weight are different for
>> boys and girls. To do this you need to create a boy dummy variable to go
>> with the girl dummy variable (I don't think you can use standard factor
>> variables in --mixed--).
>>
>> gen boy=girl==0
>>
>> tabulate girl, nolabel
>> tabulate boy, nolabel
>>
>>
>> The model is specified as:
>>
>> mixed weight age || id: boy girl, nocons
>>
>> which gives you separate random intercepts variances by boy and girl.
>>
>> ------------------------------------------------------------------------------
>> Random-effects Parameters | Estimate Std. Err. [95% Conf.
>> Interval]
>> -----------------------------+------------------------------------------------
>> id: Independent |
>> var(boy) | .7620931 .3329286 .3237065
>> 1.794174
>> var(girl) | .4330075 .2723947 .12619
>> 1.485819
>> -----------------------------+------------------------------------------------
>> var(Residual) | 1.530808 .1887532 1.202168
>> 1.94929
>> ------------------------------------------------------------------------------
>>
>> You need to suppress the overall intercept in the random effects portion or
>> you can't estimate this model (the dummy variables will be collinear with
>> the overall intercept). Indeed, try
>>
>> mixed weight age || id: boy girl
>>
>> you will see that Stata will automatically drop one of the variables.
>>
>> Hope that helps and sorry for the two emails.
>>
>> Best,
>> Scott
>>
>>
>>
>> On Wed, Jul 10, 2013 at 11:12 AM, Lucas <[email protected]> wrote:
>>>
>>> A (seemingly) simple question: What does it mean in stata to suppress
>>> the constant from "the" random effects equation?
>>>
>>> (Note: Although I use the terms here, I agree with Andrew Gelman's
>>> very clear critical observations on the terms "fixed effect" and
>>> "random effect", which can be found by googling "Why I don't use the
>>> term "fixed and random effects"").
>>>
>>> The reason I ask is that the output does not seem to provide an
>>> equation for the "random effect". We do see a variance, and if there
>>> are multiple random effects, we see variances and covariances. Thus,
>>> where is the constant (if we do not use the noconstant option)? What
>>> is the constant giving us? What does using the noconstant take away
>>> from the output? Is the constant some parameter for the variance? If
>>> so, where does it appear in the greek-equation specification of the
>>> model? (And, just for clarity-sake, why aren't the other parameters
>>> involved in the variance (e.g., the context-level Z's) in the random
>>> effects area as opposed to being forced up into the fixed effects area
>>> by interactions?)
>>>
>>> Things become a bit less clear if multiple parameters are allowed to
>>> vary across contexts (e.g., the slope for Education and the slope for
>>> Occupation vary across contexts). It seems one is allowed to specify
>>> noconstant for one, both, or none of the "random effects." But,
>>> 1)how, and, 2)what would it mean, as the random effect section of the
>>> output doesn't seem to contain constants whether one uses the
>>> noconstant option or not?
>>>
>>> I've been using the software, nut, as my work becomes more complex, I
>>> am pushed to consider many options I had not needed before. And, upon
>>> closer inspection, I realize I am confused as to the basic
>>> "noconstant" option for the "re" equation, and now this de-stabilizes
>>> my sense of what the "re" section of the output is doing (or
>>> suppressing). Although I did not read every example, I have looked
>>> over some of the -me- examples in the stata13 manual and so far have
>>> not found any cases where the noconstant option is used in the random
>>> effects equation. Perhaps I missed it, and, if so, I'd appreciate
>>> being pointed to the example(s) that might clarify matters. At any
>>> rate, these are emerging questions.
>>>
>>> Thanks a bunch.
>>> Sam
>>> *
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>>> * http://www.stata.com/support/faqs/resources/statalist-faq/
>>> * http://www.ats.ucla.edu/stat/stata/
>>
>>
> *
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