Fabien Lebugle wrote:
> I am a master student. I am currently doing an internship. I would
> like to get some advices about the following issue: I have 2 data
> sets, both containing the same variables, but the data were measured
> using two different procedures. I want to know if the two procedures
> are equivalent. Up to know, I have built one linear model for each
> dataset. The two models have the same form. I would like to compare
> these two models: are they identical? Are they different? By how
> much?
>
> Please, could you tell me which R procedure I should use? I have been
> searching the list archive, but without success...
This is not a question of ``which R procedure'' but rather a
question of understanding a bit about statistics and linear
models. You say you are a ``master's student''; I hope you
are not a master's student in *statistics*, given that you
lack this (very) basic knowledge! If you are a student in
some other discipline, I guess you may be forgiven.
The ``R procedure'' that you need to use is just lm()!
Briefly, what you need to do is combine your two data
sets into a *single* data set (using rbind should work),
add in a grouping variable (a factor with two levels,
one for each measure procedure) e.g.
my.data$gp <- factor(rep(c(1,2),c(n1,n2)))
where n1 and n2 are the sample sizes for procedure 1 and
procedure 2 respectively.
Then fit linear models with formulae involving the
grouping factor (``gp'') as well as the other predictors,
and test for the ``significance'' of the terms in
the model that contain ``gp''. You might start with
fit <- lm(y~.*gp,data=my.data)
anova(fit)
where ``y'' is (of course) your reponse.
You ought to study up on the underlying ideas of inference
for linear models, and the nature of ``factors''. John Fox's
book ``Applied Regression Analysis, Linear Models, and
Related Methods'' might be a reasonable place to start.
Bon chance.
cheers,
Rolf Turner
[EMAIL PROTECTED]
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