logistic regression or not?
array chip <arrayprofile <at> yahoo.com> writes: [snip]
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?
Sometimes I've seen it called "binomial regression", or just "a binomial generalized linear model"
Second, then summary(fit) and anova(fit, test='Chisq') gave me different p values, why is that? which one should I use?
summary(fit) gives you p-values from a Wald test. anova() gives you tests based on the Likelihood Ratio Test. In general the LRT is more accurate.
Third, is there an equivalent model where I can use variable "percentage" instead of "positive" & "total"?
glm(percentage~treatment,weights=total,data=tests,family=binomial) is equivalent to the model you fitted above.
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)?
Don't quite know what you mean here. How is the situation "almost the same as ANOVA" different from the situation you described above? Do you mean when there are multiple factors? or ???