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.
--
David.
Li Sun
2012/6/24 Uwe Ligges <lig...@statistik.tu-dortmund.de>:
On 24.06.2012 17:47, Li SUN wrote:
Hi All,
when using the linear model lm() to fit data of the form y = k * x +
b, is it possible to know the p-value for the parameters k and b?
i.e.
can we find the result of the form (k, Δk; b, Δb)?
If you explain what Δk means in a linear model, we may be able to
help.
Also, what is "the p-value of the parameters"? Obviously a
hypothesis is
missing in this sentence.
Best,
Uwe Ligges
Thanks in advance!
Li Sun
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David Winsemius, MD
West Hartford, CT
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