logit clforum idade fem habmen9 habsup resid vexpFEA voutras exp formtem s
> emin melodea cinea roteiros pateo clasexp expecta inform acolhim
Iteration 0: log likelihood = -64.481718
Iteration 1: log likelihood = -45.955808
Iteration 2: log likelihood = -43.036576
Iteration 3: log likelihood = -42.699927
Iteration 4: log likelihood = -42.687686
Iteration 5: log likelihood = -42.68766
Logit estimates Number of obs =
134
LR chi2(18) =
43.59
Prob > chi2 =
0.0007
Log likelihood = -42.68766 Pseudo R2 =
0.3380
------------------------------------------------------------------------------
clforum | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
---------+--------------------------------------------------------------------
idade | -.0379192 .0237848 -1.594 0.111 -.0845364
.0086981
fem | .142543 .5963118 0.239 0.811 -1.026207
1.311293
habmen9 | -.361584 .904894 -0.400 0.689 -2.135144
1.411976
habsup | -1.911038 .9093529 -2.102
0.036 -3.693337 -.1287392
resid | -1.386813 .8617904 -1.609 0.108 -3.075891
.3022655
vexpFEA | 1.170202 .7630049 1.534 0.125 -.3252598
2.665665
voutras | .5517524 .7542407 0.732 0.464 -.9265323
2.030037
exp | -.8654879 .9781649 -0.885 0.376 -2.782656
1.05168
formtem | .6785345 .8420722 0.806 0.420 -.9718967
2.328966
semin | .3446929 .7274 0.474 0.636 -1.080985
1.770371
melodea | 1.178986 .9043637 1.304 0.192 -.5935341
2.951506
cinea | -.6163336 .8959065 -0.688 0.491 -2.372278
1.139611
roteiros | .8907071 1.090565 0.817 0.414 -1.246761
3.028175
pateo | .6254803 .9519656 0.657 0.511 -1.240338
2.491299
clasexp | .9305477 .3527112 2.638 0.008 .2392466
1.621849
expecta | -1.105424 1.171044 -0.944 0.345 -3.400628
1.18978
inform | 1.191919 .8563612 1.392 0.164 -.4865177
2.870356
acolhim | 1.975343 1.194611 1.654 0.098 -.366051
4.316737
_cons | -1.70797 1.870524 -0.913 0.361 -5.374129
1.958189
------------------------------------------------------------------------------
With an output like this how do i calculate the probabilitys from chi
square to interpretate the coeficients?
----- Original Message -----
From: "daniel waxman" <dan@amplecat.com>
To: <statalist@hsphsun2.harvard.edu>
Sent: Monday, January 30, 2006 11:55 PM
Subject: RE: st: Unobserved heterogeneity in logistic regression
> Thanks for the response. As it turns out, things are quite consistent
> between hospitals, and between everything else... perhaps I am just being
> paranoid.
>
> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Karen Norberg
> Sent: Monday, January 30, 2006 2:48 PM
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: Unobserved heterogeneity in logistic regression
>
> Depending on the kind of heterogeneity you think might exist, one option
> is to use a 'fixed effects' or conditional logistic model. If you think
> there is unobserved heterogeneity between hospitals, you stratify the
> sample by hospital, thus 'conditioning out' of your model any
> characteristics that may vary between hospitals, but remain constant among
> observations within hospital.
>
> Hospital isn't the only thing you could condition on - you could use
> month or year fixed effects, other attributes -
>
> Karen Norberg, MD
>
>
>
> On Mon, 30 Jan 2006, Maarten buis wrote:
>
>> Dear Daniel:
>>
>> The problem with unobserved heterogeneity is that it is well...
> unobserved. Apparently you have
>> many predictors of mortality available, so an obvious solution is to add
> some of these predictors.
>> In an earlier post you suggested that your variables are collinear, so
>> you
> probably don't want to
>> add them all. That is no problem since the fact that they are collinear
> with the variables left
>> out means that most of the variance is captured by the variables in the
> model (it does make the
>> causal interpretation of these control variables more difficult, but the
> roll of control variables
>> is to control, and that is what they do).
>>
>> I see the results of my models more as a rough indication than anything
> else. So I tend to worry
>> less about technicalities like these. In my own research I deal with
> survey data, and in my
>> department they tape trained and experienced interviewers from reputable
> agencies while they are
>> interviewing and code the interactions between interviewer and
> interviewed. The results make me
>> very skeptical about the precision of my data. (See aside below) The
>> paper
> was written more to
>> satisfy my nerdish tendencies than that I thought that the impact of this
> phenomenon would be
>> large enough to be noticeable above the random noise coming from data
> collection. (I may be wrong
>> though; the simulations by Glenn Hoetker seem to point in that direction,
> though I have not yet
>> read it as carefully as I should). I pointed you to this phenomenon
> because in such a sensitivity
>> analysis this phenomenon might be worthy of a footnote, and my working
> paper might be helpful in
>> understanding the literature to which it refers (and also the literature
> to which Richard Williams
>> referred).
>>
>> So, my not entirely satisfactory answer is: dealing with "observed
> heterogeneity" is much easier
>> than unobserved heterogeneity. If you use additional modeling on top of
> that and you get different
>> results make sure you understand why that is the case and convince
> yourself that that is
>> plausible.
>>
>> HTH,
>> Maarten
>>
>> Aside
>> Taping interviews does result in some funny interactions though:
> Interviewer: How many times do
>> you eat grain products for breakfast? Respondent: Well.... never ....
> eh.... well no, that's not
>> right, beer is a grain product too, isn't it?
>>
>> More often the interactions aren't that funny. For instance, the
> "experienced" interviewer looks
>> around the room and decides for the respondent in which income and
> educational category he/she
>> falls, or asks very suggestive questions, makes mistakes while entering
> the data, etc. etc. etc.
>>
>> --- daniel waxman <dan@amplecat.com> wrote:
>>
>>> Maartin Buis directed me to a short paper of his: "Unobserved
> heterogeneity
>>> in logistic regression":
>>>
>>> http://home.fsw.vu.nl/m.buis/
>>>
>>> The concept makes sense--the question is what to do about it.
>>
>> <snip>
>>
>>> There are of course many unobserved causes for in-hospital mortality,
> but
>>> insofar as this particular model seems to work, do I need to deal with
> this?
>>> If one does try to deal with it in a situation such as mine, is it a
> matter
>>> of using a method other than simple logistic regression to fit the
> model, or
>>> is it more a matter of assessment of goodness if fit?
>>>
>>
>>
>> -----------------------------------------
>> Maarten L. Buis
>> Department of Social Research Methodology
>> Vrije Universiteit Amsterdam
>> Boelelaan 1081
>> 1081 HV Amsterdam
>> The Netherlands
>>
>> visiting adress:
>> Buitenveldertselaan 3 (Metropolitan), room Z214
>>
>> +31 20 5986715
>>
>> http://home.fsw.vu.nl/m.buis/
>> -----------------------------------------
>>
>>
>>
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