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 <d.m.mitch...@reading.ac.uk>:
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 the
extreme data.
thanks for your help Remko, its been very helpful.
Dann
Remko Duursma-2 wrote:
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(10000, shape=1.1, rate=2.5)
# now suppose you fit a gamma distribution, and get these estimated
parameters:
shapeest <- 1.101
rateest <- 2.49
h <- hist(mydata, breaks=50, plot=FALSE)
plot(h$mids, log(h$density))
curve(log(dgamma(x, shape=shapeest, rate=rateest)), add=TRUE)
#Remko
-------------------------------------------------
Remko Duursma
Post-Doctoral Fellow
Centre for Plant and Food Science
University of Western Sydney
Hawkesbury Campus
Richmond NSW 2753
Dept of Biological Science
Macquarie University
North Ryde NSW 2109
Australia
Mobile: +61 (0)422 096908
On Wed, Jan 28, 2009 at 1:13 AM, Dan31415 <d.m.mitch...@reading.ac.uk>
wrote:
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 for data.vec)
xrarea<-seq(-2,9,0.05)
yrarea<-dgamma(xrarea,shape=7.9862,rate=2.6621)
so now yrarea is the gamma distribution and i want to compare it with
data.vec to see how well it fits.
regards,
Dann
Remko Duursma-2 wrote:
Hi Dann,
there is probably a better way to do this, but this works anyway:
# your data
gamdat <- rgamma(10000, shape=1, rate=0.5)
# comparison to gamma:
gamsam <- rgamma(10000, shape=1, rate=0.6)
qqplot(gamsam,gamdat)
abline(0,1)
greetings
Remko
-------------------------------------------------
Remko Duursma
Post-Doctoral Fellow
Centre for Plant and Food Science
University of Western Sydney
Hawkesbury Campus
Richmond NSW 2753
Dept of Biological Science
Macquarie University
North Ryde NSW 2109
Australia
Mobile: +61 (0)422 096908
On Tue, Jan 27, 2009 at 3:38 AM, Dan31415 <d.m.mitch...@reading.ac.uk>
wrote:
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 for discrete distributions and kolmogorov-smirnov seems
that
large sample sizes make it had to evaluate the D statistic. Also i
haven't
found a qq plot for gamma, although i think this might be an
appropriate
test.
in summary
-is there a gamma goodness of fit test that doesnt depend on the sample
size?
-is there a way of using qqplot for gamma distributions, if so how
would
you
calculate it from a matrix of data values?
regards,
Dann
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