[R] Summary: proc mixed vs. lme

[Grathwohl,Dominik,LAUSANNE,NRC/NT]

Hi Freq

if you like to get familiar with logistic regression in R, I would
recommend the text book of Venables and Ripley (Modern Applied 
Statistics with S-PLUS). All data sets and functions 
are available for R in the MASS-library. The MASS-library could be down
loaded 
from http://cran.r-project.org/bin/windows/contrib/VR.zip (e.g.. Windows
users).
The glm function plays the key role. With the help of this function you
could fit
logistic regression models. First step in the material could be:

library(MASS)
?glm

Regards,

Dominik

> -----Original Message-----
> From: Feng Zhang [mailto:f0z6305 at labs.tamu.edu]
> Sent: mercredi, 9. octobre 2002 22:26
> To: Grathwohl,Dominik,LAUSANNE,NRC/NT; r-help at stat.math.ethz.ch
> Subject: Re: [R] Summary: proc mixed vs. lme
> 
> 
> Sorry to bother you.
> I want to know if there are some Logistical Regression
> functions in R?
> 
> Thanks.
> 
> 
> 
> ----- Original Message -----
> From: "Grathwohl,Dominik,LAUSANNE,NRC/NT"
> <dominik.grathwohl at rdls.nestle.com>
> To: <r-help at stat.math.ethz.ch>
> Sent: Wednesday, October 09, 2002 1:19 PM
> Subject: [R] Summary: proc mixed vs. lme
> 
> 
> > Summary: proc mixed vs. lme
> >
> > The objective of this summary is to help people
> > to get more familiar with the specification of
> > random effects with proc mixed or lme.
> > Very useful are the examples of Ramon Littell's book:
> > "SAS System for Mixed Models (1996)"
> > (http://ftp.sas.com/samples/A55235)
> > The same data set's are kindly made available
> > by Douglas Bates in the library(SASmixed).
> > In the help file are examples of the lme statements
> > equivalent to the proc mixed ones.
> >
> > To explain the different estimates,
> > Hein and Brian suppose to check whether both
> > analyses with SAS and R uses ML estimates
> > or REML estimates. However, this was not the problem,
> > the default in proc mixed and lme is already REML.
> > Douglas advise me to use his option:
> > options( contrasts = c(unordered = "contr.SAS",
> > ordered = contr.poly"))
> > However, I already used this option,
> > because I copied the code from the SASmixed help file.
> > Peter gave me the first hint
> > and this solves the problem:
> > To change the model formula in lme,
> > from: strength ~ Program * Time
> > to: strength ~ factor(Program) * factor(Time)
> > Now the option statement grasp!
> > For people like me who try to get familiar
> > with the specification of random effects
> > would it helpful if the help file of SASmixed
> > would be updated or the variables time and program
> > would be already introduced as factors
> > in the Weights data set.
> >
> > Thank you all for the useful advises,
> >
> > Dominik
> > 
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