On 17 May 2000 09:26:35 -0700, [EMAIL PROTECTED] (mbattagl) wrote:
> I have data that measures light intensity with a number of different
> techniques. One of the measurements (a direct measurement and "true"
> measurement of light intensity) involves lots of time, labor, and expense.
> The other techniques are more practical in the sense of time and labor, but
> are indirect measurements (based on canopy structure (density, location of
> holes in the canopy, etc). My goal is to determine if the indirect
> measurements are valid estimates of the direct measurements. However, I would
> also like to predict light intensity based on the indirect methods.
< snip >
For *your* purposes, what consitutes a valid estimate? Or, what
constitutes and invalid estimate, or estimator? Is there a
test-criterion that ought to be met? You might want to look at
"increased error" if your performance is "good". Otherwise, you will
be looking at the simple fall-off of a mediocre performance. That is,
if your current R-squared is 99%, then falling off to 98% would be a
big drop, doubling the error, relatively speaking. If your current
performance has R-squared of .25, then falling to .15 would be
(approximately) a similar doubling/halving of achievement.
Are you trying to calibrate to the same range? - then draw the
principal components (correlation) line. If there is error in both X
and Y, then the rational question might be, "How much precision is
lost, or new error is introduced, when replacing the old with the
new?"
Or are you trying to predict the other *measure* with least-squared
error? - then the regression line meets that criterion.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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