I was trying to fit my data into various models and figured out that the
fitdistr function from library
R gives me
Negative Binomial as the best-fit. Now from the wiki page, the definition is given as:
NegBin(r,p) distribution describes the probability of k failures and r successes in k+r Bernoulli(p) trials with success on the last trial.
R to perform model fitting gives me two parameters
dispersion parameter. I am not understanding how to interpret these because I cannot see these parameters on the wiki page. All I can see is the following formula:
k is the number of observations and
r=0...n. Now how do I relate these with the parameters given by
R? The help file does not provide much information either.
Also, just to say a few words about my experiment: In a social experiment that I was conducting, I was trying to count the number of people each user contacted in a period of 10 days. The population size was 100 for the experiment.
Now, if the model fits the Negative Binomial, I can blindly say that it follows that distribution but I really want to understand the intuitive meaning behind this. What does it mean to say that the number of people contacted by my test subjects follows a negative binomial distribution? Can someone please help clarify this?