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From |
Steve Samuels <sjsamuels@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: transformation of continuos variable |

Date |
Wed, 14 Apr 2010 08:13:39 -0400 |

There is also this fundamental misunderstanding. If you fit model log Y a + bX b* = antilog(b) = exp(b) is always positive, and so zero will never be inside the transformed CI for b* Steve On Wed, Apr 14, 2010 at 3:54 AM, Maarten buis <maartenbuis@yahoo.co.uk> wrote: > --- On Tue, 13/4/10, riyadh shamsan wrote: >> I am using STATA 10. I did a linear regression on log transformed >> variable. To present the result i anti-logged the results but now the >> confidence interval is a bit confusing as it doesn't cross 0 > > By anti-logging your predictions you did not create predictions on > the original unit but conditional geometric means, which is probably > not what you want. The reason is that you moddeled how the > log-transformed dependent variable changes when your independent > variables change, while you probably wanted to model how the dependent > variable changes (in a possible non-linear way) when the independent > variables change. There are ways of correcting the predictions, but > the better way is to avoid the problem by estimating the right model > from the start by using -glm- in combination with the -link(log)- > option. See for example: > > Nicholas J. Cox, Jeff Warburton, Alona Armstrong, Victoria J. Holliday > (2007) "Fitting concentration and load rating curves with generalized > linear models" Earth Surface Processes and Landforms, 33(1):25--39. > <http://www3.interscience.wiley.com/journal/114281617/abstract> > > So to give a concrete example. In the example below you can see that > someone who is white, with no education, no experience, and without > union membership can expect an hourly wage of 1.66 dollars (the > baseline). Union membership lead to an increase of wage by a factor > of 1.10 (that is, 10%), a year extra education leads to an increase > in wage by a factor of 1.08 (i.e. 8%) and begin black leads to a > change in wage by a factor of .91 (i.e. -9%). > > In order to create predictions in Stata 10 while keeping some of the > covariates constant, it is convenient to use the -adjust- command. So > in the example below the graph shows how the expected wage for white > union members with average work experience, change over education. > > *-------------- begin example --------------- > sysuse nlsw88, clear > gen byte baseline = 1 > gen byte black = race == 2 if race != . > glm wage grade union ttl_exp black baseline, /// > link(log) eform nocons > > preserve > adjust union=1 black=0 ttl_exp, /// > by(grade) ci exp replace > > twoway rarea lb ub grade || /// > line exp grade, legend(off) /// > ytitle(predicted hourly wage) > restore > > *------------- end example ------------------- > (For more on examples I sent to the Statalist see: > http://www.maartenbuis.nl/example_faq ) > > Hope this helps, > Maarten > > -------------------------- > 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/ > -- Steven Samuels sjsamuels@gmail.com 18 Cantine's Island Saugerties NY 12477 USA Voice: 845-246-0774 Fax: 206-202-4783 * * 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/

**References**:**st: transformation of continuos variable***From:*riyadh shamsan <onlineriyadh@gmail.com>

**Re: st: transformation of continuos variable***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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