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

From   "Vladimir V. Dashkeyev" <>
Subject   Re: st: Graphs With Log Scale: A Bug?
Date   Wed, 28 May 2008 16:47:25 +0400


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,

On Wed, May 28, 2008 at 4:21 PM, Nick Cox <> 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" <> 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.
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