I have a Genomic data that shows the interaction between genomic regions that I would like to understand which interactions are significant statistically.
Dataset look likes:
chr start1 end1 start2 end2 normalized count 1 500 1000 2000 3000 1.5 1 500 1000 4500 5000 3.2 1 2500 3500 1000 2000 4
So, I selected a random number of data (as background) and fitted the normalized frequency into the Weibull distribution using
fitdistrplus R packages and estimated some parameters like scale and shape for those sets of data
(fit.weibull= fitdist(data$normalized count,'weibull')).
Now I would like to calculate the probability of each observation (like a p-value for each data point) under the fitted Weibull distribution?
if I have 100 data point with normalized count like:
xs <- seq(10, 65, len=100)
How can I calculate the probability of those set?
Can I use one of the below functions? and which one is better?
pweibull(xs , fit.weibull[shape], fit.weibull[scale], lower.tail = TRUE) qweibull(xs , fit.weibull[shape], fit.weibull[scale], lower.tail = TRUE)
or I should calculate CDF for each value (from those 100 point) then estimate p-value=1-CDF(x)?
is pweibull () and 1- CDF() are the same?