Re: st: transformation of continuos variable

[Steve Samuels <sjsamuels@gmail.com>]

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
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> *   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

*
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