Hello Everybody,
I have a problem with the output of lm.
In fact, when I compare the R output and the Splus output, the results
appears to be different :
I have a dataframe with 5 factors , 16 rows and a response.
First, I tried to fit this response to a linear model :
lm(rep~(A+B+C+D+E),data).
The results beetween SPlus and R are different.
In a second time, I tried a new fit : lm(rep~(A+B+D+E+A:B),data).
The estimation of the main effects in the model are the same as the one
obtained for the first fit for Splus,
and are different for R.
Here an exemple :
On R: On Splus
lm(rep~(A+B+C+D+E),data) -> Intercept 12.6297
12.5573
A 0.0391 0.0195
B -0.0073 -0.0036
C 0.0443 0.0221
D -0.0321 -0.016
E -0.18887 -0.0944
lm(rep~(A+B+D+E+A:B),data) -> Intercept 12.7937
12.5573
A -0.2445 0.0195
B -0.2910 -0.0036
D -0.03213 -0.016
E -0.18887 -0.0944
A:B 0.5672 0.1418
Can someone explain what happens with these fitting procedure.
Thanks
Isabelle Zabalza-Mezghani
--
Isabelle Zabalza-Mezghani Tel : 01 47 52 61 99
Institut Fran?ais du P?trole E-mail : isabelle.zabalza-mezghani at ifp.fr
1-4 Av. Bois Preau - Bat Lauriers
92852 Rueil Malmaison Cedex, France
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>>>>> "isabelle" == isabelle Zabalza-Mezghani <isabelle.zabalza-mezghani at ifp.fr> writes:isabelle> Hello Everybody, I have a problem with the output of lm. In isabelle> fact, when I compare the R output and the Splus output, the isabelle> results appears to be different : isabelle> I have a dataframe with 5 factors , 16 rows and a response. isabelle> First, I tried to fit this response to a linear model : isabelle> lm(rep~(A+B+C+D+E),data). The results beetween SPlus and R isabelle> are different. isabelle> In a second time, I tried a new fit : isabelle> lm(rep~(A+B+D+E+A:B),data). The estimation of the main isabelle> effects in the model are the same as the one obtained for the isabelle> first fit for Splus, and are different for R. isabelle> Here an exemple : ...omitted... isabelle> Can someone explain what happens with these fitting isabelle> procedure. It's because your "A",.. are factors, and the default contrasts (for non-ordered factors) are different in R from those in S-plus (on purpose!): R uses "treatment" contrasts by default (intuitive to most users) S-plus has "helmert" ones " " (neat for orthogonality reasons, somewhat hard to interpret) See ?contrasts (and read Venables & Ripley ..) isabelle> Thanks isabelle> Isabelle Zabalza-Mezghani (and then, the whole stuff once more, with <HTML> tags .. Isabelle, please re-configure your E-mail program to *NOT* send html, at least not to mailing lists ...) Martin Maechler <maechler at stat.math.ethz.ch> http://stat.ethz.ch/~maechler/ Seminar fuer Statistik, ETH-Zentrum LEO D10 Leonhardstr. 27 ETH (Federal Inst. Technology) 8092 Zurich SWITZERLAND phone: x-41-1-632-3408 fax: ...-1228 <>< -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Dear Isabelle,
R and S-Plus have different default options for constructing contrasts
(columns of the model matrix) from factors. For S-Plus:
> options("contrasts")
$contrasts:
factor ordered
"contr.helmert" "contr.poly"
For R:
> options("contrasts")
$contrasts
unordered ordered
"contr.treatment" "contr.poly"
By changing the contrasts option you should be able to get R and S-Plus to
agree. Help in R and S-Plus provides additional information about contrast
codings.
I hope that this helps,
John
At 10:22 AM 26/10/2000 +0200, you wrote:>Hello Everybody,
>
>I have a problem with the output of lm.
>In fact, when I compare the R output and the Splus output, the results
>appears to be different :
>
>I have a dataframe with 5 factors , 16 rows and a response.
>First, I tried to fit this response to a linear model :
>lm(rep~(A+B+C+D+E),data).
>The results beetween SPlus and R are different.
>
>In a second time, I tried a new fit : lm(rep~(A+B+D+E+A:B),data).
>The estimation of the main effects in the model are the same as the one
>obtained for the first fit for Splus,
>and are different for R.
>
>Here an exemple :
>
>
>On R: On Splus
>
> lm(rep~(A+B+C+D+E),data)
> -> Intercept 12.6297 12.5573
> A
> 0.0391 0.0195> B
> -0.0073 -0.0036> C
> 0.0443 0.0221> D
> -0.0321 -0.016> E
> -0.18887 -0.0944>
> lm(rep~(A+B+D+E+A:B),data) -> Intercept 12.7937
12.5573
> A
> -0.2445 0.0195> B
> -0.2910 -0.0036> D
> -0.03213 -0.016> E
> -0.18887 -0.0944> A:B
> 0.5672 0.1418>
>Can someone explain what happens with these fitting procedure.
>
>Thanks
>
>
>Isabelle Zabalza-Mezghani
>
>
>
>
>--
>Isabelle Zabalza-Mezghani Tel : 01 47 52 61 99
>Institut Fran?ais du P?trole E-mail : isabelle.zabalza-mezghani at
ifp.fr
>1-4 Av. Bois Preau - Bat Lauriers
>92852 Rueil Malmaison Cedex, France
>
________________________________
John Fox
Department of Sociology
McMaster University
email: jfox at McMaster.ca
web: www.socsci.mcmaster.ca/jfox
________________________________
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