Suppose I have the variable x that was generated using the following approach:
x <- rgamma(100,2,11) + rnorm(100,0,.01) #gamma distr + some gaussian noise
head(x,20)
[1] 0.35135058 0.12784251 0.23770365 0.13095612 0.18796901 0.18251968
[7] 0.20506117 0.25298286 0.11888596 0.07953969 0.09763770 0.28698417
[13] 0.07647302 0.17489578 0.02594517 0.14016041 0.04102864 0.13677059
[19] 0.18963015 0.23626828
How could I fit a gamma distribution to it?
You could try to quickly fit Gamma distribution. Being two-parameters distribution one could recover them by finding sample mean and variance. Here you could have some samples to be negative as soon as mean is positive.
set.seed(31234)
x <- rgamma(100, 2.0, 11.0) + rnorm(100, 0, .01) #gamma distr + some gaussian noise
#print(x)
m <- mean(x)
v <- var(x)
print(m)
print(v)
scale <- v/m
shape <- m*m/v
print(shape)
print(1.0/scale)
For me it prints
> print(shape)
[1] 2.066785
> print(1.0/scale)
[1] 11.57765
>