On Jul 28, 2011, at 9:13 AM, m.fen...@libero.it wrote:
Dear R users,
I'd like to known your opinion about a problem with anova.lmRob() of
"Robust" package that occurs when I run a lmRob() regression on my
dataset.
I check my univariate model by single object anova as
anova(lmRob(y~x)).
If I compare my model with the null model (y~1), I must obtain the
same results,
but not for my data.
Is it possible?
My example:
x<-c(rep(0,8),rep(1,8),rep(2,7))
y<-
c
(1,0.6
,-0.8,0.7,1.6
,-0.2
,-1.2
,-3.8
,-1.8
,-2.6,-1.7,-2.1,-0.3,-1.4,1.4,-0.3,-0.3,0.5,0.4,-0.9,-1.6,0.4,0.4)
library(robust)
lmR<-lmRob(y~factor(x))
anova(lmR)
lmR0<-lmRob(y~1)
anova(lmR,lmR0)
If I run the code omitting the factor() (then treating "x" as
continuous), the results are the same..
I do not get the same results with that code. And the code does not
appear to track your description, since the second model does not have
an "x" term in it. Even when I create the model that it sounded as
though you would have written, namely lmR0 <- lmRob(y ~ x), it is
clearly _not_ the same result.
> coef(lmR)
(Intercept) factor(x)1 factor(x)2
0.2428571 -1.3432007 -0.4000000
> coef(lmR0)
(Intercept) x
-0.509524217 0.005820355
What is the explanation of these different results?
Since you didn't post your results and since your complaint was that
they are "the same", it's hard to know what you are talking about.
--
David Winsemius, MD
West Hartford, CT
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