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Revision as of 17:34, 3 November 2011
, 17:34, 3 November 2011→R Lattice Graphics
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== A Fancier QQ Plot by Matthew Flickinger ==
Unfortunately the simple way of doing it leaves out many of the things that are nice to have on the plot such as a reference line and a confidence interval plus if your data set is large it plots a lot of points that aren't very interesting in the lower left. Here is a more complex example that adds a few more niceties and thins the data to only plot meaningful points
Unfortunately the simple way of doing it leaves out many of the things that are nice to have on the plot such as a reference line and a confidence interval plus if your data set is large it plots a lot of points that aren't very interesting in the lower left. Here is a more complex example that adds a few more niceties and thins the data to only plot meaningful points
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=== Sample Usage ===
A sample call to this function would be
A sample call to this function would be
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qqunif.plot(my.pvalues) #these are the raw p-values, not log-transformed
qqunif.plot(my.pvalues) #these are the raw p-values, not log-transformed
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The confidence intervals are calculated using the fact that the standard uniform order statistics follow a beta distribution. The default settings will draw confidence intervals around the 1000 more significant points. You can change that with the <code>conf.points=</code> parameter and you can change the alpha level from the default .05 using the <tt>conf.alpha=</tt> parameter. If you wish to disable the confidence interval, use <tt>draw.conf=F</tt> in your call to <tt>qqunif.plot()</tt>.
The confidence intervals are calculated using the fact that the standard uniform order statistics follow a beta distribution. The default settings will draw confidence intervals around the 1000 more significant points. You can change that with the <tt>conf.points=</tt> parameter and you can change the alpha level from the default .05 using the <tt>conf.alpha=</tt> parameter. If you wish to disable the confidence interval, use <tt>draw.conf=F</tt> in your call to <tt>qqunif.plot()</tt>.
This function does thin the data by rounding the observer and expected -log10 p-values to two places by default. You can control the thinning with the <tt>should.thin=</tt>, <tt>thin.obs.places=</tt>, and <tt>thin.exp.places=</tt> parameters.
This function does thin the data by rounding the observer and expected -log10 p-values to two places by default. You can control the thinning with the <tt>should.thin=</tt>, <tt>thin.obs.places=</tt>, and <tt>thin.exp.places=</tt> parameters.
