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Re: st: margins after xtlogit
From
Traci Schlesinger <[email protected]>
To
[email protected]
Subject
Re: st: margins after xtlogit
Date
Sat, 11 Sep 2010 01:24:44 -0500
Thanks, Michael. This makes perfect sense now.
Cheers,
Traci
On Sat, Sep 11, 2010 at 12:41 AM, Michael N. Mitchell
<[email protected]> wrote:
> Dear Traci
>
> I will start with a possible answer, and then work back to why this is so.
> After the -xtlogit- command, try this...
>
> margins race1, predict(pu0)
>
> This should then express the results in terms of predicted probabilities
> (as the -logit- model did). The reason is that the -predict- command
> defaults to predicting probabilities in after the -logit- command. This is
> described in
> -help logit postestimation- under the section on predict that will say
> "pr probability of a positive outcome; the default"
>
> Contrast this with -help xtlogit postestimation-, in which the section
> about predict says that the default prediction is "xb linear
> prediction; the default".
>
> I hope that helps,
>
> Michael N. Mitchell
> Data Management Using Stata - http://www.stata.com/bookstore/dmus.html
> A Visual Guide to Stata Graphics - http://www.stata.com/bookstore/vgsg.html
> Stata tidbit of the week - http://www.MichaelNormanMitchell.com
>
>
>
> On 2010-09-10 4.59 PM, Traci Schlesinger wrote:
>>
>> hi all:
>>
>> i am analyzing racial disparities in pretrial diversions (a yes no,
>> i.e. 0/1, criminal justice outcome) using individual level data from
>> the SCPS, which is clustered by county--an observation for every
>> individual charged with a felony in sampled counties is included. to
>> account for the county level sampling, i'm using xtlogit with county
>> level random effects.
>>
>> however, i'm having difficulty interpreting the results from margins
>> after xtlogit.
>>
>> if i estimate a model with logistic and then ask for margins on race i
>> get:
>>
>> . margins race1, post
>>
>> Predictive margins Number of obs =
>> 46019
>> Model VCE : OIM
>>
>> Expression : Pr(diversion), predict()
>>
>>
>> ------------------------------------------------------------------------------
>> | Delta-method
>> | Margin Std. Err. z P>|z| [95% Conf.
>> Interval]
>>
>> -------------+----------------------------------------------------------------
>> race1 |
>> 1 | .1025184 .0023145 44.29 0.000 .097982
>> .1070548
>> 2 | .0848741 .0020596 41.21 0.000 .0808374
>> .0889109
>> 3 | .0858849 .0023203 37.01 0.000 .0813372
>> .0904327
>>
>>
>> ------------------------------------------------------------------------------
>>
>> which i interpret as meaning that if everyone in my sample were white
>> (race1 = 1), 10% of defendants would be offered pretrial diversions.
>> if everyone were black (race1=2), only 8% of defendants would be
>> offered pretrial diversions. (race1=3 are Latinos, with 8.5% of
>> people getting diversions).
>>
>> however, if i estimate xtlogit --either getting my results as
>> coefficients or odds-ratios-- and then margins, i get the following
>> table.
>>
>> . margins race1, post
>>
>> Predictive margins Number of obs =
>> 46019
>> Model VCE : OIM
>>
>> Expression : Linear prediction, predict()
>>
>>
>> ------------------------------------------------------------------------------
>> | Delta-method
>> | Margin Std. Err. z P>|z| [95% Conf.
>> Interval]
>>
>> -------------+----------------------------------------------------------------
>> race1 |
>> 1 | -3.580741 .2277247 -15.72 0.000 -4.027073
>> -3.134409
>> 2 | -3.919428 .2274633 -17.23 0.000 -4.365248
>> -3.473608
>> 3 | -3.67982 .2301685 -15.99 0.000 -4.130942
>> -3.228698
>>
>> ------------------------------------------------------------------------------
>>
>> i am at a loss as to how to interpret this. for starters, it seems
>> strange that all three racial groups have negative margins. also, i'm
>> clearly not looking at the percent of defendants who get a pretiral
>> diversion any more. i've looked through the manual, but have not been
>> able to figure this out. i would appreciate any help.
>>
>> cheers,
>> traci
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