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 <jasonkrup...@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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> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
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