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

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: predict |

Date |
Mon, 6 Jun 2011 10:34:11 +0200 |

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) * * 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>

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

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