it is easy to make a qqplot for the gamma; suppose that the sample parameters
are 1.101 and 2.49, the data in x:
plot(qgamma(ppoints(x),1.101,2.49),sort(x))
see also lattice:qqmath
albyn
Quoting Dan31415 :
Ah yes, that does produce a nice plot. Can i just ask what exactly it is
sho
Ah yes, that does produce a nice plot. Can i just ask what exactly it is
showing. It seems to me to be a sort of Q-Q plot but with a different set of
axes. Is this correct, if so do the same interpretation rules apply for this
plot, i.e. departures from either end of the curve show poor fitting of
It sounds like you just want to graph it though. For gammas, it's nice
to graph the log of the density, because
the tail is so thin and long, so you don't see much otherwise:
mydata <- rgamma(1, shape=1.1, rate=2.5)
# now suppose you fit a gamma distribution, and get these estimated parameter
Thanks for that Remko, but im slightly confused because isnt this testing the
goodness of fit of 2 slightly different gamma distributions, not of how well
a gamma distribution is representing the data.
e.g.
data.vec<-as.vector(data)
(do some mle to find the parameters of a gamma distribution fo
Hi Dann,
there is probably a better way to do this, but this works anyway:
# your data
gamdat <- rgamma(1, shape=1, rate=0.5)
# comparison to gamma:
gamsam <- rgamma(1, shape=1, rate=0.6)
qqplot(gamsam,gamdat)
abline(0,1)
greetings
Remko
-
I'm looking for goodness of fit tests for gamma distributions with large data
sizes. I have a matrix with around 10,000 data values in it and i have
fitted a gamma distribution over a histogram of the data.
The problem is testing how well that distribution fits. Chi-squared seems to
be used more
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