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From |
"Clive Nicholas" <clivelists@googlemail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: marginal effect of clogit |

Date |
Sat, 22 Sep 2007 10:48:40 +0100 |

Somsupa Nopprach wrote: > I have read that -mfx- can not use with conditional > logit model. So i decide to show my result by using > the odds ratio instead of marginal effect. Do you > think this is appropriate? Taken from -help j_mfxunsuit-: Predict expression unsuitable error message A marginal effect is a derivative of a function of the coefficients and independent variables of a model. The mfx option predict() can be used to specify the function to be differentiated. If this function depends on quantities other than the coefficients and independent variables of the model, mfx will most likely not be able to evaluate the derivative accurately. mfx checks for dependence on other quantities by predicting into different observations in the dataset and checking that it obtains the same value. If not, it determines that the expression is unsuitable. The mfx option diagnostics(beta) displays the results of these checks. The option force can be used to force the calculation of the marginal effect, although this is usually inadvisable. > However, I have another problem. my model also > includes interactive terms between two dummies. and I > also have read that the interpretation of odds ratio > is not appropriate for interactive or multiplicative > variables. > Any one knows any useful command in stata in order > that i can solve this problem? You have probably read the same literature on this subject as I have. I came to the conclusion that there are no quick-fix solutions to this problem. You either decide that running such models are 'tolerable' or you decide that they are not. However, one partial solution I have found helpful is to use Richard Williams' -oglm- routine, with the use of the -het()- option. This is downloadable from SSC. -mfx- can be used after -oglm-. See the example below, with the error messages deliberately shown. Note that -southXt- is the interaction term for -south- and -t0-. . webuse union . tsset idcode year . xtlogit union age grade south black t0 southXt, i(id) fe note: multiple positive outcomes within groups encountered. note: 2744 groups (14165 obs) dropped due to all positive or all negative outcomes. note: black omitted due to no within-group variance. Iteration 0: log likelihood = -4516.2771 Iteration 1: log likelihood = -4510.9103 Iteration 2: log likelihood = -4510.9077 Iteration 3: log likelihood = -4510.9077 Conditional fixed-effects logistic regression Number of obs = 12035 Group variable (i): idcode Number of groups = 1690 Obs per group: min = 2 avg = 7.1 max = 12 LR chi2(5) = 78.56 Log likelihood = -4510.9077 Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ union | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .0707478 .0960376 0.74 0.461 -.1174825 .2589781 grade | .0814976 .0419064 1.94 0.052 -.0006373 .1636325 south | -1.005429 .1496154 -6.72 0.000 -1.29867 -.7121886 t0 | -.0633121 .0967567 -0.65 0.513 -.2529516 .1263275 southXt | .0263557 .0083166 3.17 0.002 .0100554 .0426561 ------------------------------------------------------------------------------ . mfx, predict(p outcome(1)) equation 1 not found r(303); . mfx, predict(p) diagnostics(beta) Predict into observation 1 = .13628032 Predict into last observation = .16871637 Predict into all observations: mean = .14042376 Predict into all observations: sd = .06756762 predict() expression p unsuitable for marginal-effect calculation r(119); . oglm union age grade south black t0, het(southXt) Heteroskedastic Ordered Logistic Regression Number of obs = 26200 LR chi2(6) = 1208.84 Prob > chi2 = 0.0000 Log likelihood = -13259.808 Pseudo R2 = 0.0436 ------------------------------------------------------------------------------ | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- union | age | .0278939 .0051979 5.37 0.000 .0177061 .0380816 grade | .0783001 .0067902 11.53 0.000 .0649915 .0916087 south | -1.021978 .0645004 -15.84 0.000 -1.148396 -.8955593 black | .8861081 .0369666 23.97 0.000 .8136549 .9585613 t0 | -.0223223 .0059469 -3.75 0.000 -.0339779 -.0106666 -------------+---------------------------------------------------------------- lnsigma | southXt | .0060774 .0030963 1.96 0.050 8.71e-06 .0121461 -------------+---------------------------------------------------------------- /cut1 | 2.833318 .1436359 19.73 0.000 2.551797 3.114839 ------------------------------------------------------------------------------ . scalar lnsigma = [lnsigma]_b[southXt] . display "Allison's delta = " (1 - exp(lnsigma))/ exp(lnsigma) Allison's delta = -.00605897 . mfx, predict(p outcome(1)) Marginal effects after oglm y = Pr(union==1) (predict, p outcome(1)) = .20706878 ------------------------------------------------------------------------------ variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X ---------+-------------------------------------------------------------------- age | .0044708 .00083 5.39 0.000 .002846 .006095 30.4322 grade | .0125498 .00107 11.70 0.000 .010448 .014651 12.7615 south*| -.1558508 .00744 -20.94 0.000 -.170439 -.141263 .413015 black*| .1576848 .0069 22.87 0.000 .144168 .171201 .274542 t0 | -.0035778 .00094 -3.82 0.000 -.005412 -.001743 9.47137 southXt | .0013398 .00068 1.96 0.050 -1.4e-07 .00268 3.96874 ------------------------------------------------------------------------------ (*) dy/dx is for discrete change of dummy variable from 0 to 1 The postestimation code used after -oglm, het()- was kindly provided by Richard himself. -oglm- is only a partial solution because, together with the added code, it provides extra information in the form of 'Allison's delta', a measure of how large or small the disturbance variance is for one group over the other group being compared (Allison, 1994: 193). However, it can only indicate to you how problematic the use of comparing logit coefficients across your two groups is likely to be in your model: it cannot 'fix' any problem for you. In the above example, 100 * -.00605897 = -0.61, so we conclude that the difference in disturbance variance between 'southerners' and 'nonsoutherners' is small. I wouldn't mind seeing Richard coming onto this thread to say whether or not he plans to incorporate the added code into -oglm-. I think it would be very useful. -- Clive Nicholas [Please DO NOT mail me personally here, but at <clivenicholas@hotmail.com>. Thanks!] Allison P (1994) "Comparing Logit and Probit Coefficients Across Groups", Sociological Methods and Research 28(2): 186-208. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: marginal effect of clogit***From:*somsupa Nopprach <nmayecon31@yahoo.com>

**References**:**st: marginal effect of clogit***From:*somsupa Nopprach <nmayecon31@yahoo.com>

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