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From |
Chiara Mussida <cmussida@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: predict |

Date |
Mon, 6 Jun 2011 10:55:43 +0200 |

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/

**Follow-Ups**:**Re: st: predict***From:*Maarten Buis <maartenlbuis@gmail.com>

**References**:**st: predict***From:*Chiara Mussida <cmussida@gmail.com>

**Re: st: predict***From:*Richard Williams <richardwilliams.ndu@gmail.com>

**Re: st: predict***From:*Chiara Mussida <cmussida@gmail.com>

**Re: st: predict***From:*Chiara Mussida <cmussida@gmail.com>

**Re: st: predict***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: predict***From:*Chiara Mussida <cmussida@gmail.com>

**Re: st: predict***From:*Maarten Buis <maartenlbuis@gmail.com>

**Re: st: predict***From:*Chiara Mussida <cmussida@gmail.com>

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