[R] Help - lm, glm, aov results inconsistent with other stati stical package
David Hugh-Jones
davidhughjones at gmail.com
Wed Mar 1 10:56:33 CET 2006
Perhaps it's worth mentioning that you can view and edit the contrasts
for a particular factor by doing e.g.
contrasts(A)
contrasts(A) = contr.treatment(levels(A), base=2) # make the second
category the base
cheers
David
On 01/03/06, Christoph Buser <buser at stat.math.ethz.ch> wrote:
> Dear Ben
>
> Berwin is correct in his answer about different
> parameterizations.
>
> After changing the contrasts in R from treatment to sum
>
> options(contrasts = c("contr.sum", "contr.poly" ))
> test.lm<-lm(y~A*x)
> summary(test.lm)
>
> I got similar results to JMP. Be careful in doing a correct
> interpretation of your coefficients, especially when you have an
> interaction in your model.
>
> Regards,
>
> Christoph Buser
>
> --------------------------------------------------------------
> Christoph Buser <buser at stat.math.ethz.ch>
> Seminar fuer Statistik, LEO C13
> ETH (Federal Inst. Technology) 8092 Zurich SWITZERLAND
> phone: x-41-44-632-4673 fax: 632-1228
> http://stat.ethz.ch/~buser/
> --------------------------------------------------------------
>
>
>
>
> Ben Ridenhour writes:
> > Okay, I took the data to SAS and it gives me the same answer that R does. I don't why JMP is giving me an incorrect answer, but it seems to be. (Either that or I have made the same mistake in SAS and R.) Any ideas what JMP might be doing?
> >
> > Ben
> >
> > -------------------------------
> > Benjamin Ridenhour
> > School of Biological Sciences
> > Washigton State University
> > P.O. Box 644236
> > Pullman, WA 99164-4236
> > Phone (509)335-7218
> > --------------------------------
> > "Nothing in biology makes sense except in the light of evolution."
> > -T. Dobzhansky
> >
> >
> > ----- Original Message ----
> >
> > To: "Liaw, Andy" <andy_liaw at merck.com>; r-help at stat.math.ethz.ch
> > Sent: Tuesday, February 28, 2006 10:50:18 PM
> > Subject: Re: [R] Help - lm, glm, aov results inconsistent with other stati stical package
> >
> > Alright, I'll try to give some sample code.
> >
> > # create A with 2 levels - 2 and 4
> > > A<-c(rep(2,times=30),rep(4,times=42))
> >
> > # make A a factor
> > > A<-as.factor(A)
> >
> > #generate 72 random x points
> > > x<-rnorm(72)
> >
> > #create different slopes & intercepts for A=2 and A=4
> > #add a random error term
> > > y<-c(x[(1:30)]+1,2*x[(31:72)]-2)+rnorm(72,mean=0,sd=2)
> >
> > #use model y~A*x for lm (or glm)
> > > test.lm<-lm(y~A*x)
> >
> > #check the output
> > > summary(test.lm)
> >
> > This essentially creates something like my data set and uses the same model. In response to (1), I was just using 0/1 because these are codings for the 2 levels, correct? (i.e., when A=2 the dummy variable=0, when A=4 the dummy variable=1?). In response to (2), yes, I do want different slopes for the two categories (that is what I am interested in testing).
> >
> > If I export the data created above to JMP and run what I believe to be the same model, I get a different answer for my equations :(
> >
> >
> > -------------------------------
> > Benjamin Ridenhour
> > School of Biological Sciences
> > Washigton State University
> > P.O. Box 644236
> > Pullman, WA 99164-4236
> > Phone (509)335-7218
> > --------------------------------
> > "Nothing in biology makes sense except in the light of evolution."
> > -T. Dobzhansky
> >
> >
> > ----- Original Message ----
> > From: "Liaw, Andy" <andy_liaw at merck.com>
> >
> > Sent: Tuesday, February 28, 2006 5:14:57 PM
> > Subject: RE: [R] Help - lm, glm, aov results inconsistent with other stati stical package
> >
> > 1. You have levels(A) as "2" and "4", yet you showed equations for A=0 and
> > A=1?
> >
> > 2. y = A + X + A*X means you're allowing the different groups of A to have
> > different slopes. Probably not what you intended.
> >
> > 3. It's probably best to provide a small sample of the data (and R code) so
> > we know how you got what you got.
> >
> > Andy
> >
> > From: Ben Ridenhour
> > >
> > > Hello,
> > >
> > > I 'm sure there must a be a simple explanation for what I'm
> > > doing wrong but I am stumped. I am a novice R user and this
> > > has shaken my confidence in what I'm doing! I am trying to
> > > run a simple ANCOVA using the model y~A*x, where y and x are
> > > continuous and A has two levels. Everything seems fine until
> > > I compare the output with what I get from another statistical
> > > package (JMP). JMP has the model y=A+x+A*x (this should be
> > > the same as what I specified to R, correct?). In comparison
> > > I get the line equations
> > >
> > > y = 7072.09-1024.94 x (for A=0) and
> > > y = 7875.58-970.088 x (for A=1)
> > >
> > > from JMP. And from R I get
> > >
> > > y = 6276.7-1259.8 x (for A=0) and
> > > y = 7867.5-1150.1 x (for A=1).
> > >
> > > Obviously, these aren't even close to the same answer. I've
> > > tried this using glm(), lm(), and aov() and as expected they
> > > all give the same answer. If I do
> > >
> > > >levels(A)
> > > [1] "2" "4"
> > >
> > > which are the two levels of A. Why can't I get the same
> > > answer from JMP as in R? This is very disturbing to me!
> > >
> > > Thanks,
> > > Ben
> > >
> > >
> > > -------------------------------
> > > Benjamin Ridenhour
> > > School of Biological Sciences
> > > Washigton State University
> > > P.O. Box 644236
> > > Pullman, WA 99164-4236
> > > Phone (509)335-7218
> > > --------------------------------
> > > "Nothing in biology makes sense except in the light of evolution."
> > > -T. Dobzhansky
> > >
> > >
> > >
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> > >
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> > >
> > >
> >
> >
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