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.
>
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