On 24/06/2012 18:39, David Winsemius wrote:
On Jun 24, 2012, at 1:21 PM, Li SUN wrote:
Sorry for the confusion.
Let me state the question again. I missed something in my original
statement.
When using the linear model lm() to fit data of the form y = k * x +
b, where k, b are the coefficients to be found, and x is the variable
and has an error bar (uncertainty) Δx of the same length associated
with it. Is it possible to pass Δx to the linear model lm(), and from
the output to find the uncertainty Δk for k, Δb for b as well?
In one sense this could be done if you were interpreting the "Δx" as the
vector of individual residuals of a model, but I'm guessing that might
not be what you meant. You would be able to recover the original data,
assuming you knew the X values, and would proceed by calculating the Y
values as the sum of predictions and the residuals, thus recovering the
original data. But I'm guessing you want to supply a small number of
parameters from an analysis you are reading about and you are hoping to
be getting from lm() further information to answer some question. That's
not the direction of teh flow of information. The flow is data INTO
lm(), estimation of parameters OUT.
Show us a sample dataset constructed with R code or show us the console
output of dput() applied to your dataset, and you may get better answers
to what is still an unclear question.
This is not linear regression if 'x' is not known exactly. There are
various formulations of the problem, but that is off-topic here.
However, consulting
@Book{Fuller.87,
author = "Fuller, Wayne A.",
title = "Measurement Error Models",
publisher = "John Wiley and Sons",
address = "New York",
year = "1987",
ISBN = "0-471-86187-1",
}
would be a good start.
--
Brian D. Ripley, rip...@stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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