Dear R community,
I am new to R, a reforming SAS user :) I am running R 2.10.1 on a Windows XP
machine. I would like to write linear functions of my coefficient parameter
estimates from a glm, but am having a difficult time understanding the
parameterization R uses. In the toy example below I am running a glm on
binomial data, with clones and lines within clones as fixed effects, each with
6 replicates. I cannot figure out the algorithm R uses for determining which
combination of levels is used as the reference. Crawley, in the chapter on
Statistical Modeling in the R Book states "The writers of R agree that
treatment contrasts represent the best solution. This method does away with
parameter a, the overall mean. The mean of the factor level that comes first in
the alphabet (control, in our example) is promoted to pole position, and the
other effects are shown as differences (contrasts) between this mean and the
other four factor level means."
This pattern seems to hold for full factorials, but it doesn't appear to work
with this example in which Line is nested within Clone. In this example, R
appears to use the last line in alphabet order of the first clone (Clone 1) as
the intercept. Thereafter, the reference levels for Clones are the last lines
of Clones 2 and 3, but the first line (Line 1) of Clone 4. The first line of
Clone 4, is also the first line over all lines. Can someone please explain
what process R uses to determine the parameterization of this model? Of
course, I know what the parameterization is in this model and can write the
functions I need. However, I am trying to understand the algorithm R uses to
determine the parameterization. This is only a toy example of a much larger
study and I would like to know when I should use the last level as the
reference and when I should use the first.
Many thanks,
Manuela
tmp <- data.frame(Cl=rep(1:4,c(12,18,18,18)),
L =rep(c(91,104,14,91,96,"84-1",96,116,1,9,14),each=6),
N =rep(10,(12+18*3)),
D =c(1,6,8,1,1,1,2,6,10,3,3,1,2,1,1,0,4,4,6,5,3,5,1,3,
1,6,8,5,5,3,5,2,1,0,4,5,1,0,2,3,6,7,4,0,2,5,3,8,1,
4,7,0,6,3,7,2,3,6,1,9,7,2,1,3,0,1) )
tmp$N[c(13,15,59)] <- c(8,9,9) # not always 10 trials
tmp$Cl <- factor(tmp$Cl) # Make sure clone and line within clone are
factors
tmp$L <- factor(tmp$L)
model <- formula(cbind(D,(N-D))~ Cl/L)
tmp.glm<-glm(data=tmp,formula=model, family=quasibinomial(link="logit"))
with(tmp,table(L,Cl))
summary.glm(tmp.glm)$coefficients
unique(tmp[order(tmp$L),1:2])
# The coeff estimates R gives are in this order,
# but R is using the rows c(1,8,10,11) as reference
# Why?
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
