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From |
"Vladimir V. Dashkeyev" <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Graphs With Log Scale: A Bug? |

Date |
Wed, 28 May 2008 16:47:25 +0400 |

Nick, Thanks for the answer. I did not use -predict- since this approach does not provide a quick way for drawing confidence intervals. If I'm wrong and there is a way to draw CI, please, let me know about it. Thank you, Vladimir On Wed, May 28, 2008 at 4:21 PM, Nick Cox <[email protected]> wrote: > My advice is to use -predict- after each model fitted to save the results in separate variables. Then draw one graph to get you want. I wouldn't approach this via -lfit- or > -lfitci-. That will also oblige you to make explicit what you are doing. > > Nick > > Vladimir V. Dashkeyev > > Thanks for the reply. I should have emphasized in the first message, > that I run -lfitci- of X on ln(Y) in both scenarios. The difference is > in the scatter plot. In the first scenario I use ln(Y), and in the > second -- Y with log scale option. I expected to get the same linear > prediction line and the same scatter plot. > > But after I posted that question, I compared the graphs once again and > realized that the real problem is with the Y axis scale. If I draw a > scatter and prediction line on the same Y axis, everything is fine. > Yet if I draw the same scatter with 2 Y axes I get different range of > values on Y1 and Y2 axes. I need two Y axes for overlaid drawing of > the scatter with -yscale (log)- option and linear prediction of > X-ln(Y). Setting range on both axes to the same values did not help. > They are very close but still shifted a bit. So the arrangement of > observations and prediction line is not correct. So it's not a bug, > but still a problem I have to solve. > > Is there any way to "tie" axis Y1 with axis Y2? > > Maarten buis > >> --- "Vladimir V. Dashkeyev" <[email protected]> wrote: >>> I drew a two-way plot with a linear prediction line -lfitci- of X on >>> natural logarithm of Y. Next, I drew the plot of X on Y with log >>> scale option -yscale(log)-. >>> >>> To my surprise regression line changed its slope. The slope is >>> greater with the -yscale(log)- option. I used the same X axis and >>> the second Y-axis for the linear prediction graph . >>> Is this a bug or am I doing something wrong? >> >> This is not a bug: in the first scenario you are thinking that there is >> a linear relationship between ln(Y) and X and you are showing the >> predictions, while in the second scenario you are thingking that there >> is a linear relationship between Y and X and then transforme the >> predictions to a log scale. So the results are different because the >> models are different. >> > > * > * 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/ > * * 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: Graphs With Log Scale: A Bug?***From:*"Nick Cox" <[email protected]>

**References**:**st: Graphs With Log Scale: A Bug?***From:*"Vladimir V. Dashkeyev" <[email protected]>

**Re: st: Graphs With Log Scale: A Bug?***From:*Maarten buis <[email protected]>

**Re: st: Graphs With Log Scale: A Bug?***From:*"Vladimir V. Dashkeyev" <[email protected]>

**RE: st: Graphs With Log Scale: A Bug?***From:*"Nick Cox" <[email protected]>

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