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Re: st: Estout after xtmixed with ar(1) residuals
From
Tom Trikalinos <[email protected]>
To
[email protected]
Subject
Re: st: Estout after xtmixed with ar(1) residuals
Date
Mon, 22 Mar 2010 10:53:08 -0400
read kit's post for good suggestions if not solutions.
kali epityxia apo kardias.
take care,
t
On Mon, Mar 22, 2010 at 7:48 AM, Dimitris Pavlopoulos
<[email protected]> wrote:
> Thoma,
>
> I double checked it with my data and you are absolutely correct. I'll try to find out how to apply this with estout.
>
> eyxaristo,
> Dimitris
> ________________________________________
> From: [email protected] [[email protected]] On Behalf Of Tom Trikalinos [[email protected]]
> Sent: Sunday, March 21, 2010 3:06 PM
> To: [email protected]
> Subject: Re: st: Estout after xtmixed with ar(1) residuals
>
> Dimitri,
>
> It appears that Stata has parameterized their optimization a bit
> differently than you (and I) expected.
>
> r_lns2ose is NOT the log(SE) of the residuals in level 1 of the
> grouping variable.
> It appears that Stata parameterizes the SE of the residuals in level 1
> of the grouping variable using the values at level 0 of the grouping
> variable as a reference.
>
> log(SE_level1_of_grouping) = lnsig_e + r_lns2ose (i.e., r_lns2ose is
> the log of the factor that multiplied by the SE of the residuals at
> level 0 of the grouping variable, gives you the desired SE).
>
> not sure how you do it in estout. If there is no easier way, you can
> always calculate the needed value, add it to the estimation results
> and then run estout.
>
> take care
> t
>
> I run this silly example:
>
> **************Code begins ********************
>
> webuse nlswork
> // make a grouping variable with 2 levels
> gen race2 = (race>=2)
>
>
> xtmixed ln_w grade age tenure if id<1000 || id: grade, ///
> res(ar 1, t(year) by(race2))
>
> // store the the numbers in the betas
> mat betas = e(b)
>
> // these are the random effects parameters -- exp transformation, as you stated
> di exp(el(betas,1,colnumb(betas,"lns1_1_1:_cons")))
> di exp(el(betas,1,colnumb(betas,"lns1_1_2:_cons")))
>
> // this is the standard deviation of the residual group 1 -- exp
> transformation of
> // lnsig_e + r_lns2ose
> di exp(el(betas,1,colnumb(betas,"lnsig_e:_cons"))+el(betas,1,colnumb(betas,"r_lns2ose:_cons")))
>
> // you already know how to get the correlations.
>
> ****************code ends**********************
>
>
>
>
>
>
>
>
> On Sun, Mar 21, 2010 at 8:20 AM, Dimitris Pavlopoulos
> <[email protected]> wrote:
>> Dear Thoma,
>>
>> thank you for your reply. I am doing what you suggest but it doesn't work.
>>
>> Specifically, in estout, I am using the transformation "transform(ln*: exp(@) exp(@) r_ln*: exp(@) exp(@) at*: tanh(@) (1-tanh(@)^2) r_atr*: tanh(@) (1-tanh(@)^2))", so I am calculating exp(r_lns2ose). Apparently it's not the correct transormation.
>>
>> best regards,
>> Dimitris
>>
>>
>>
>> ________________________________________
>> From: [email protected] [[email protected]] On Behalf Of Tom Trikalinos [[email protected]]
>> Sent: Thursday, March 18, 2010 5:23 PM
>> To: [email protected]
>> Subject: Re: st: Estout after xtmixed with ar(1) residuals
>>
>> Just guessing here -- you should be able to work out quickly if I'm
>> right or wrong.
>>
>> Typically in an ML routine you would optimize for t=log( of an SD or a variance)
>> this guarantees that the exp(t) (the SD or the variance) is non negative.
>>
>> So I'm guessing that to get your variance or SE it should be exp(r_lns2ose)
>>
>> Similarly for correlations. If they are allowed to range between -1
>> and 1, one probably optimizes for z=tanh^-1 of the correlation, as the
>> tanh(z) is bounded between -1 and 1.
>> However, in an autoregressive model the correlation should be between
>> 0 and 1, no? So I would program the optimization so that the
>> back-transformation is 0.5*tanh(z)+0.5 -- bounded between 0 and 1. I
>> dunno what Stata does.
>>
>> Please, send an e-mail to the list when you've figured out.
>>
>>
>>
>> Thomas A Trikalinos MD, PhD
>>
>> Co-Director Tufts Evidence-based Practice Center
>> Associate Director, Center for Clinical Evidence Synthesis
>>
>> Institute for Clinical Research and Health Policy Studies
>> Tufts Medical Center | 800 Washington St | Boston, 02111 MA
>>
>> Phone: +1 617 636 0734
>> Fax: +1 617 636 8628
>> email: [email protected]
>>
>>
>>
>>
>> On Thu, Mar 18, 2010 at 8:17 AM, Dimitris Pavlopoulos
>> <[email protected]> wrote:
>>> Dear all,
>>>
>>> I am trying to use estout after xtmixed (STATA 11 /SE). I am using a model with random slopes and an ar(1) stucture for the residuals. Stata 11 allows the estimation of separate correlation and residual standard deviation according to the values of a categorical variable. I have used this feature for a binary variable.
>>>
>>> My problem is that I cannot find which function is estout using to transform the standard deviation of the residuals that corresponds to value 1 of the grouping variable. So, for the residuals estout present:
>>>
>>> lnsig_e: variance for residuals corresponding to value 0 of grouping variable
>>> r_atr1: autoregressive correlation corresponding to value 0 of grouping variable
>>> r_lns2ose: variance for residuals corresponding to value 1 of grouping variable
>>> r_atr2: autoregressive correlation corresponding to value 1 of grouping variable
>>>
>>> for lnsig_e, I need to exponantiate to get the standard deviation
>>> For r_atr1 and r_atr2, I am using the tanh() function.
>>>
>>> Does anybody know what function do I have to use to get the standard deviation from r_lns2ose?
>>>
>>> Thanks in advance?
>>>
>>> best regards,
>>> Dimitris
>>> *
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>>
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>
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