What about trying a qqplot to see how the distribution fits...
For the normal distribution thta is very stright forward, use qqnorm.
To test gamma distribtution (or any other) do some thing like this
n<-length(data)
for(i in 1:n){
prob<-(i-1/3)/(n1/3)
}
quantiles<-qgamma(prob,shape=mean(data)/var(data),scale=var(data)/mean(data)}
qqplot(data,quantiles)
If the distribution is a good for, you should a stright line, like wiht a
qqnorm plot!
Good luck!!
Elizbaeth Lawson
David Zhao <[EMAIL PROTECTED]> wrote:
Hi there,
I'm a newbie, plesae bear with me.
I have a dataset with about 10000 ~ 30000 data points. Would like fit to
both Gamma and Normal distribution to see which one fits better. How do I do
this in R? Or I could do a normality test of the data, if it's normal, I
then will do a normal fit, otherwise, a gamma fit. But again, I don't know
how to do this either.
Please help!
David
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