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
"treatment" "total" "positive" "percentage"
"1" "exposed" 11 4 0.363636363636364
"2" "exposed" 10 4 0.4
"3" "exposed" 9 4 0.444444444444444
"4" "exposed" 7 4 0.571428571428571
"5" "exposed" 7 4 0.571428571428571
"6" "exposed" 6 5 0.833333333333333
"8" "exposed" 12 7 0.583333333333333
"9" "exposed" 8 5 0.625
"10" "exposed" 13 12 0.923076923076923
"11" "exposed" 10 5 0.5
"12" "control" 10 1 0.1
"13" "control" 11 2 0.181818181818182
"14" "control" 8 0 0
"16" "control" 12 1 0.0833333333333333
"15" "control" 8 0 0
"17" "control" 10 1 0.1
"18" "control" 10 1 0.1
"19" "control" 8 1 0.125
"20" "control" 8 0 0
"21" "control" 9 1 0.111111111111111
"22" "control" 10 1 0.1
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