Re: st: predict

[Chiara Mussida <cmussida@gmail.com>]

Likely there is a way to automatically compute predicted prob in STATA
(alternative to predict) to get:

pr1 = exp(b0 + b1 x1)/(exp(b0 + b1 x1) + exp(b0 + b2 x2) + 1)

Thanks
Chiara


On 6 June 2011 10:34, Chiara Mussida <cmussida@gmail.com> wrote:
> The code I use was,
> mlogit utr sex age loweduc compulsory diploma, b(3)
>
> then I got my estimates in STATA.
>
> by typing:
> predict p1 if e(sample), outcome(1)
>
> I did get a probability different from the one I got by using the
> coefficient estimates to compute the relative odds ratio.
> Many Thanks
> Chiara
>
>
> On 6 June 2011 10:18, Maarten Buis <maartenlbuis@gmail.com> wrote:
>> The reason is that you made an error in your computations. Since you
>> did not give use the code you used for your computations we cannot
>> tell you what that error is.
>>
>> -- Maarten
>>
>> On Mon, Jun 6, 2011 at 10:07 AM, Chiara Mussida <cmussida@gmail.com> wrote:
>>> Dear All,
>>> many thanks to Maarten and Richard for their precious help.
>>> One doubt remain unsolved:
>>> when I compute the predicted probabilities from my mlogit as:
>>>
>>> pr1 = exp(b0 + b1 x1)/(exp(b0 + b1 x1) + exp(b0 + b2 x2) + 1)
>>>
>>> where pr1 is the predicted prob of outcome 1, b0 is a constant, b1 and
>>> b2 the coefficients from outcome 1 and 2. here I assume that outcome 3
>>> is the base category, and a totalo of three outcomes.
>>>
>>> this computation, carried out by using the coefficients of the STATA
>>> output (mlogit commands) differs from the outcome predicted by using
>>> the predict command (which is a mlogit postestimation outcome), such
>>> as:
>>> Predict probabilities of outcome 1 for estimation sample
>>> predict p1 if e(sample), outcome(1)
>>>
>>> my question is: why the two computations offer different results for
>>> predicted probabilities? Maybe related to the method of computation
>>> behind predict command.
>>>
>>> Many Thanks
>>> C
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> On 3 June 2011 09:42, Maarten Buis <maartenlbuis@gmail.com> wrote:
>>>> --- On 2 June 2011 18:08, Chiara Mussida wrote:
>>>>> I simply want the coefficients (of my covariates) which allow me to
>>>>> get the predicted outcome of each equation of my MNL.
>>>>>
>>>>> example: I get a predicted probability (say to move from employment to
>>>>> unemployment) of 0.4:
>>>>> what is the contribution (numerical) of each covariate I included in
>>>>> my equation (suc as sex, individual age, etc.). Is it given by the
>>>>> exponential of the coef I find in the Stata output? therefore by
>>>>> summing/subtracting the exp of each coef I get my predicted of 0.4
>>>>> (but there is also a standard error)
>>>>
>>>> The contribution of each variable to the predicted probability is
>>>> neither its coefficient nor the exponential of that coefficient. It is
>>>> a non-linear function you can find in any introductory text on
>>>> multinomial regression. So you cannot use a set of additions of
>>>> coefficients to get to the predicted probability.
>>>>
>>>> If you want to give a exact representation of the model you will have
>>>> to look at relative risks or odds(*) (**), this is:
>>>>
>>>> relative risk = exp(b0 + b1 x1 + b2 x2 + ...)
>>>>
>>>> or, equivalently
>>>>
>>>> relative risk = exp(b0) * exp(b1 x1) * exp(b2 x2) * ...
>>>>
>>>> Alternatively, you can fit a linear model on top of your multinomial
>>>> logistic regression, and use those results to summarize the results.
>>>> This is what you do when you compute marginal effects. As this is the
>>>> result of a model on top of a model it will not be an exact
>>>> representation of the original multinomial regression model, so the
>>>> addition of coefficients will in all likelihood lead to deviations
>>>> from the actual predicted probabilities. on the plus side, you can now
>>>> interpret your results in terms of probabilities instead of relative
>>>> risks.
>>>>
>>>> The fact that marginal effects are not exact representation of the
>>>> model results is not necessarily bad. Marginal effects form a model of
>>>> your multinomial regression model, and models aren't supposed to be
>>>> exact, they are only supposed to be useful. Whether or not this model
>>>> of a model is useful depends on the exact aim of the exercise. If you
>>>> do this in order to compute some kind of decomposition of effects,
>>>> than I would stick to the exact representation, if I were presenting
>>>> results than I would look at who my audience is. There are also cases
>>>> where the underlying multinomial regression model is so complicated,
>>>> that the linear approximation implicit in the marginal effects starts
>>>> to struggle. For example it is not uncommon for correctly computed
>>>> marginal effects of interaction terms to be significantly positive for
>>>> some respondents, significantly negative for others, and
>>>> non-significant for the remaining respondents. In most cases, that is
>>>> hardly a useful conclusion.
>>>>
>>>> Hope this helps,
>>>> Maarten
>>>>
>>>> (*) There are some differences between disciplines in whether the
>>>> outcomes of a multinomial logistic regression can be called an odds or
>>>> whether a new term like relative risk has to be invented for it. See,
>>>> for example: <http://www.stata.com/statalist/archive/2007-02/msg00085.html>
>>>>
>>>> (**) Notice that I say here relative risk or odds, I did not say
>>>> relative risk ratio or odds ratio. It is a common mistake to assume
>>>> that these things are the same.
>>>>
>>>>
>>>> --------------------------
>>>> Maarten L. Buis
>>>> Institut fuer Soziologie
>>>> Universitaet Tuebingen
>>>> Wilhelmstrasse 36
>>>> 72074 Tuebingen
>>>> Germany
>>>>
>>>>
>>>> http://www.maartenbuis.nl
>>>> --------------------------
>>>> *
>>>> *   For searches and help try:
>>>> *   http://www.stata.com/help.cgi?search
>>>> *   http://www.stata.com/support/statalist/faq
>>>> *   http://www.ats.ucla.edu/stat/stata/
>>>>
>>>
>>>
>>>
>>> --
>>> Chiara Mussida
>>> PhD candidate
>>> Doctoral school of Economic Policy
>>> Catholic University, Piacenza (Italy)
>>>
>>> *
>>> *   For searches and help try:
>>> *   http://www.stata.com/help.cgi?search
>>> *   http://www.stata.com/support/statalist/faq
>>> *   http://www.ats.ucla.edu/stat/stata/
>>>
>>
>>
>>
>> --
>> --------------------------
>> Maarten L. Buis
>> Institut fuer Soziologie
>> Universitaet Tuebingen
>> Wilhelmstrasse 36
>> 72074 Tuebingen
>> Germany
>>
>>
>> http://www.maartenbuis.nl
>> --------------------------
>>
>> *
>> *   For searches and help try:
>> *   http://www.stata.com/help.cgi?search
>> *   http://www.stata.com/support/statalist/faq
>> *   http://www.ats.ucla.edu/stat/stata/
>>
>
>
>
> --
> Chiara Mussida
> PhD candidate
> Doctoral school of Economic Policy
> Catholic University, Piacenza (Italy)
>



-- 
Chiara Mussida
PhD candidate
Doctoral school of Economic Policy
Catholic University, Piacenza (Italy)

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/