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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: transformation of continuos variable |

Date |
Wed, 14 Apr 2010 07:54:03 +0000 (GMT) |

--- 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/

**Follow-Ups**:**Re: st: transformation of continuos variable***From:*Steve Samuels <sjsamuels@gmail.com>

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

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