[R] logistic regression or not?

[array chip]

Hi, I have a dataset where the response for each person on one of the 2 
treatments was a proportion (percentage of certain number of markers being 
positive), I also have the number of positive & negative markers available for 
each person. what is the best way to analyze this kind of data?

I can think of analyzing this data using glm() with the attached dataset:

test<-read.table('test.txt',sep='\t')
fit<-glm(cbind(positive,total-positive)~treatment,test,family=binomial)
summary(fit)
anova(fit, test='Chisq')

First, is this still called logistic regression or something else? I thought 
with logistic regression, the response variable is a binary factor?

Second, then summary(fit) and anova(fit, test='Chisq') gave me different p 
values, why is that? which one should I use?

Third, is there an equivalent model where I can use variable "percentage" 
instead of "positive" & "total"?

Finally, what is the best way to analyze this kind of dataset where it's almost 
the same as ANOVA except that the response variable is a proportion (or success 
and failure)?

Thanks

John



      
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