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From |
"Daniel Waxman" <dan@amplecat.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: confidence intervals for ratio of predictions-- bootstrap vs. parametric methods? |

Date |
Fri, 12 Oct 2007 21:31:49 -0400 |

For the record, I realized that I was bootstrapping the wrong thing. Here is (a minimally simplified version) of what I meant to do ... . boot rr_univ=(invlogit($mb_notindicated_predictors)/invlogit($mbneg_predictors)), /* */ reps($reps) saving(multiboot_notneg_$i,replace): /* */ logistic outcome zlog zero mbpos int_zlog_pos . estat bootstrap (no more red 'x's) So the question was ... how to explain that these CIs appear so much better than those generated as follows? . logistic outcome zlog zero mbpos int_zlog_pos . predictnl rr=invlogit($mbpos_predictors)/invlogit($mbneg_predictors),se(se_rr) . gen ub = rr + 1.96*se_rr . gen lb = rr - 1.96*se (and is it reasonable to assume that with a whole lot of reps, the bias-corrected bootstrapped CIs are in fact better?) Where: . global mbpos_terms _b[_cons] + _b[int_zlog_pos]*`constant' + /* */ _b[int_zero_pos] + _b[zlog]*`constant' + _b[zero] + _b[mbpos] . global mbneg_terms _b[_cons] + _b[zlog]*`constant' + _b[zero] Perhaps the question is more clear now (?) Daniel * * 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: confidence intervals for ratio of predictions-- bootstrap vs. parametric methods?***From:*"Daniel Waxman" <dan@amplecat.com>

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