[R] QQ plotting of various distributions...
Petar Milin
pmilin at ff.uns.ac.rs
Sun Sep 27 12:11:24 CEST 2009
Thanks for the answer. Now, only problem is to to get parameter(s) of a
given function. For gamma, I shall try with gammafit() from mhsmm
package. Also, I shall look for others appropriate parameter estimates.
Will use SuppDists too.
Best,
PM
Sunil Suchindran wrote:
> #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 at 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
>
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