# Re: [R] Goodness fit test HELP!

From: Charles Annis, P.E. <Charles.Annis_at_statisticalengineering.com>
Date: Sun 04 Dec 2005 - 08:05:57 EST

The nice thing about the uniform density is that it's easy to know what the expected pdf(pmf) should look like, namely each observation should have probability 1/n. That means you can use "qqplot." See ?qqplot

Here's an example, using "my.data."

my.data <- runif(100)
n.points <- length(my.data)
expected.cdf <- ((1:n.points)-0.5)/(n.points) qqplot(my.data, expected.cdf, las=1)
# Use the "interocular trauma test" for goodness-of-fit: my.lm <- lm(expected.cdf ~ sort(my.data)) abline(coef=coef(my.lm), lty=2)

Charles Annis, P.E.

Charles.Annis@StatisticalEngineering.com phone: 561-352-9699
eFax: 614-455-3265
http://www.StatisticalEngineering.com

If I have a Uniform distribution to check, How can I use visual fits? Can I also use in some way the qqnorm?

Thanks

-----Original Message-----
From: r-help-bounces@stat.math.ethz.ch
[mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Elizabeth Lawson Sent: Friday, November 18, 2005 8:06 PM
To: David Zhao
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Goodness fit test HELP!

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 <wzhao6898@gmail.com> 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.

David

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