Jim Silverton gmail.com> writes:
>
> Hi all,
>
> I have the following data:
>
> rep1_treat rep2_treat rep1_control rep2_control
> 2 3 4 5
> 100 20 98 54
> 0 1 2 3
> 23 3227 28
>
> Two replicates for the treatment and control groups. I want to simulate from
> the null where the null is:
> Ho:there is no difference between control and treatment groups.
>
> Can R do a glm to do this?
> Another point is my data is overdispersed, so I would like to fit a negative
> binomial glm for each variable. Each row is a variable.
Your data look a little weird. Do you really have (0,2) and
(100,23) as responses for rep1_treat, or are those indices that
got mangled somehow? In any case, you need to rearrange your
data to long format:
d <- data.frame(rep1_treat=c(2,100,0,23),
rep2_treat=c(3,20,1,32),
rep1_control=c(4,98,2,27),
rep2_control=c(5,54,3,28))
library(reshape)
d2 <- melt(d)
d3 <- data.frame(d2,colsplit(d2$variable,"_",c("rep","ttt")))
library(lattice)
xyplot(value~ttt:rep,data=d3)
library(MASS)
g1 <- glm.nb(value~1,data=d3)
simulate(g1) ## simulate from null model
g2 <- glm.nb(value~ttt,data=d3)
You may need to consider the possibility that "rep1" and "rep2"
are different. In principle 'rep' should be treated as a random
effect, but with only two reps that's not really feasible, so
try including it as an interaction instead (i.e. value~rep*ttt)
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