First time writing in here, so I apologize if something is missing.
I am comparing two DNA quantification methods and I am trying to see which method gives me results closer to the real ones (the DNA that I added to the samples).
For each method I have 5 replicates. I put 1000 cells in each sample (expected value) and got the following quantification values (observed values):
- Method P - 500, 400, 400, 500, 500
- Method Q - 1000, 900, 1400, 700, 1000
When I try to use the chisq() function, I appear not to be able to tell the function which are the expected values, it calculates the expected values and that is not what I want.
> P<-c(500, 400, 400, 500, 500)
> Q<-c(1000, 900, 1400, 700, 1000)
> chisqQ <- chisq.test(Q)
Chi-squared test for given probabilities
data: Q
X-squared = 260, df = 4, p-value < 2.2e-16
> chisqP <- chisq.test(P)
Chi-squared test for given probabilities
data: P
X-squared = 26.087, df = 4, p-value = 3.039e-05
The problem with this is that I do not establish my expected values, and while for the Q it automatically calculates 1000, for the P it doesn't
> round(chisqQ$expected,2)
[1] 1000 1000 1000 1000 1000
> round(chisqP$expected,2)
[1] 460 460 460 460 460
There is the p argument in the chisq function, but it has to be a probability, which is not my case.
I have calculated the chi square values by hand on excel and compare them, but once, in the future, I will have several techniques and several cell amounts I would like to know if it is possible to do in R.
Thanks in advance,
Cheers,
Joana