[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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