[R] Website, book, paper, etc. that shows example plots of distributions?

[Gabor Grothendieck]

You might also want to look at the idealized situation:

library(playwith)
library(sn)

playwith(qqnorm(qsn(1:99/100, shape = shape)),
       parameters = list(shape = seq(-3, 3, .1)))


On Fri, Feb 13, 2009 at 6:43 AM, Gabor Grothendieck
<ggrothendieck at gmail.com> wrote:
> You can readily create a dynamic display for using qqplot and similar functions
> in conjunction with either the playwith or TeachingDemos packages.
>
> For example, to investigate the effect of the shape parameter in the skew
> normal distribution on its qqplot relative to the normal distribution:
>
>   library(playwith)
>   library(sn)
>   playwith(qqnorm(rsn(100, shape = shape)),
>       parameters = list(shape = seq(-3, 3, .1)))
>
> Now move the slider located at the bottom of the window that
> appears and watch the plot change in response to changing
> the shape value.
>
> You can find more distributions here:
> http://cran.r-project.org/web/views/Distributions.html
>
> On Thu, Feb 12, 2009 at 1:04 PM, Jason Rupert <jasonkrupert at yahoo.com> wrote:
>> By any chance is any one aware of a website, book, paper, etc. or combinations of those sources that show plots of different distributions?
>>
>> After reading a pretty good whitepaper I became aware of the benefit of I the benefit of doing Q-Q plots and histograms to help assess a distribution.   The whitepaper is called:
>> "Univariate Analysis and Normality Test Using SAS, Stata, and SPSS*" , (c) 2002-2008 The Trustees of Indiana University Univariate Analysis and Normality Test: 1, Hun Myoung Park
>>
>> Unfortunately the white paper does not provide an extensive amount of example distributions plotted using Q-Q plots and histograms, so I am curious if there is a "portfolio"-type  website or other whitepaper shows examples of various types of distributions.
>>
>> It would be helpful to see a bunch of Q-Q plots and their associated histograms to get an idea of how the distribution looks in comparison against the Gaussian.
>>
>> I think seeing the plot really helps.
>>
>> Thank you for any insights.
>>
>>
>>
>>        [[alternative HTML version deleted]]
>>
>>
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>