#same shape

some_data <- rgamma(500,shape=6,scale=2)
test_data <- rgamma(500,shape=6,scale=2)
plot(sort(some_data),sort(test_data))
# You can also use qqplot(some_data,test_data)
abline(0,1)

# different shape

some_data <- rgamma(500,shape=6,scale=2)
test_data <- rgamma(500,shape=4,scale=2)
plot(sort(some_data),sort(test_data))
abline(0,1)

It is helpful to assess the sampling variability, by
creating repeated sets of test_data, and plotting
all of these along with your observations to create
a confidence "envelope".

The SuppDists provides Inverse Gauss.


On Thu, Sep 17, 2009 at 11:46 AM, Petar Milin <pmilin@ff.uns.ac.rs> wrote:

> Hello!
> I am trying with this question again:
> I would like to test few distributional assumptions for some behavioral
> response data. There are few theories about true distribution of those data,
> like: normal, lognormal, gamma, ex-Gaussian (exponential-Gaussian), Wald
> (inverse Gaussian) etc. The best way would be via qq-plot, to show to
> students differences. First two are trivial:
> qqnorm(dat$X)
> qqnorm(log(dat$X))
> Then, things are getting more "hairy". I am not sure how to make plots for
> the rest. I tried gamma with:
> qqmath(~ X, data=dat, distribution=function(X)
>   qgamma(X, shape, scale))
> Which should be the same as:
> plot(qgamma(ppoints(dat$X), shape, scale), sort(dat$X))
> Shape and scale parameters I got via mhsmm package that has gammafit() for
> shape and scale parameters estimation.
> Am I on right track? Does anyone know how to plot the rest: ex-Gaussian
> (exponential-Gaussian), Wald (inverse Gaussian)?
>
> Thanks,
> PM
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html<http://www.r-project.org/posting-guide.html>
> and provide commented, minimal, self-contained, reproducible code.
>

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