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From |
"Newson, Roger B" <r.newson@imperial.ac.uk> |

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

Subject |
st: RE: interpretting log transformed co-efficients |

Date |
Mon, 9 Feb 2009 11:33:48 -0000 |

If you are regressing a log-transformed outcome on one or more X-variates using -regerss-, then you should probably use the -eform- option. This implies that the coefficients displayed are geometric means, or geometric mean ratios, or geometric mean per-unit ratios (assuming an exponential relationship between the original untransformed Y-variable and the X-variable. For instance, if the X-variable is female gender, and the untransformed Y-variable is length of stay, then the coefficient for female gender is the geometric mean ratio between length of stay in females and length of stay in otherwise equivalent males. This principle is explained in a Stata Tip in the Stata Journal (Newson, 2003). If you want the exponentiated intercept (equal in your case to the geometric mean length of stay im males), then it is a good idea to use the -noconst- option, and to define a second X-variate containing values all equal to 1, whose coefficient is the exponentiated intercept. I hope this helps. Best wishes Roger References Newson R. Stata tip 1: The eform() option of regress. The Stata Journal 2003; 3(4): 445. Download from http://www.stata-journal.com/article.html?article=st0054 Roger B Newson BSc MSc DPhil Lecturer in Medical Statistics Respiratory Epidemiology and Public Health Group National Heart and Lung Institute Imperial College London Royal Brompton Campus Room 33, Emmanuel Kaye Building 1B Manresa Road London SW3 6LR UNITED KINGDOM Tel: +44 (0)20 7352 8121 ext 3381 Fax: +44 (0)20 7351 8322 Email: r.newson@imperial.ac.uk Web page: http://www.imperial.ac.uk/nhli/r.newson/ Departmental Web page: http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/pop genetics/reph/ Opinions expressed are those of the author, not of the institution. -----Original Message----- From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Ashwin Ananthakrishnan Sent: 08 February 2009 16:35 To: statalist@hsphsun2.harvard.edu Subject: st: interpretting log transformed co-efficients Hi, I'm having some trouble interpretting the linear regression co-efficients for log transformed variables. I have outcomes (such as length of stay or costs) that are not normally distributed, so I'm including the log transformed (now normal) variables as the outcome measures in linear regression models. But I'm not really sure how to interpret the resulting co-efficients. Do they represent a % change in outcome for a defined change in a predictor variable? Just for example, suppose I'm modelling length of stay against gender (male 0 female 1). Without log transformation, if I get a linear regression co-efficient of 0.6, I can say that females have a 0.6 days longer stay. But if I use log (length of stay) as the outcome and get a co-efficient 0.2 for the same linear regression model, how do I interpret this? Thanks. * * 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/ * * 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**:**st: RE: RE: interpretting log transformed co-efficients***From:*"Lachenbruch, Peter" <Peter.Lachenbruch@oregonstate.edu>

**References**:**st: interpretting log transformed co-efficients***From:*Ashwin Ananthakrishnan <ashwinna@yahoo.com>

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