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From |
"Scheetz, Marc" <mschee@midwestern.edu> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
st: Negative probabilities after a margins command for a categorical variable (post logistic model) |

Date |
Fri, 11 Oct 2013 19:19:59 +0000 |

Dear Listserv, I am wondering if anyone can help explain the scenario below to me. I am using the margins command after a multivariate-logistic model with the outcome of “died”. I am attempting to characterize the probabilities of death according to each categorical increase of the variable “log2X”. The referent category below is 2^0=1. My question is that I receive 95% CIs that have negative margins in 2 of the categories (i.e. 2._at: log2X=1, 4._at:log2X= 3). Perhaps this is a rudimentary question, but I thought that probabilities calculated from Odds Ratios could not be negative. Is this because it is a probability relative to the referent category? Do you see other errors in my syntax (below)? Many thanks in advance, Marc . logistic died i.log2X a2_day0 log10_days_to_pos_cx note: 4.log2X != 0 predicts failure perfectly 4.log2X dropped and 5 obs not used note: 5.log2X != 0 predicts failure perfectly 5.log2X dropped and 3 obs not used Logistic regression Number of obs = 83 LR chi2(6) = 18.58 Prob > chi2 = 0.0049 Log likelihood = -35.358908 Pseudo R2 = 0.2081 -------------------------------------------------------------------------------------- died | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] ---------------------+---------------------------------------------------------------- log2X | 1 | .7903086 .92746 -0.20 0.841 .0792275 7.883466 2 | 6.471551 5.420137 2.23 0.026 1.253427 33.41317 3 | 1.587899 1.492738 0.49 0.623 .2515548 10.02335 4 | 1 (empty) 5 | 1 (empty) 6 | 6.542207 6.159993 1.99 0.046 1.033362 41.41868 | a2_day0 | 1.075268 .0732118 1.07 0.286 .9409374 1.228775 log10_days_to_pos_cx | 4.854903 3.054503 2.51 0.012 1.41462 16.66177 _cons | .012261 .0189665 -2.85 0.004 .0005913 .2542394 . margins, at(log2X=(0(1)6)) Predictive margins Number of obs = 83 Model VCE : OIM Expression : Pr(died), predict() 1._at : log2X = 0 2._at : log2X = 1 3._at : log2X = 2 4._at : log2X = 3 5._at : log2X = 4 6._at : log2X = 5 7._at : log2X = 6 ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1518137 .0514472 2.95 0.003 .0509791 .2526484 2 | .1259233 .1108515 1.14 0.256 -.0913416 .3431883 3 | .4764486 .1462235 3.26 0.001 .1898558 .7630413 4 | .2137861 .1241819 1.72 0.085 -.0296061 .4571782 5 | . (not estimable) 6 | . (not estimable) 7 | .4787175 .1765856 2.71 0.007 .132616 .824819 ------------------------------------------------------------------------------ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Restructuring the time dimension in a dataset***From:*Tunga Kantarcı <tungakantarci@gmail.com>

**Re: st: Restructuring the time dimension in a dataset***From:*Maarten Buis <maartenlbuis@gmail.com>

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