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The easiest way to create a -log10 qq-plot is with the qqmath function in the lattice package. It can make a quantile-quantile plot for any distribution as long as you supply it with the correct quantile function. Many of the quantile functions for the standard distributions are built in (qnorm, qt, qbeta, qgamma, qunif, etc). However, we must specify the correct function for the -log10 uniform ourself. Here is some code which will do that with some sample data:

The easiest way to create a -log10 qq-plot is with the qqmath function in the lattice package. It can make a quantile-quantile plot for any distribution as long as you supply it with the correct quantile function. Many of the quantile functions for the standard distributions are built in (qnorm, qt, qbeta, qgamma, qunif, etc). However, we must specify the correct function for the -log10 uniform ourself. Here is some code which will do that with some sample data:

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#FAKE SAMPLE DATA

#FAKE SAMPLE DATA

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 <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>.

 

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>.

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.

qqunif.plot(my.pvalue.list, auto.key=list(corner=c(.95,.05)))

qqunif.plot(my.pvalue.list, auto.key=list(corner=c(.95,.05)))

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Note that the confidence interval drawn depends on the total number of p-values given. When you pass in a list, the number of tests the confidence interval uses is determined by the vector with the '''least number of p-values''' - this gives the widest, most conservative confidence bands. Internally the different groups are drawn using the lattice superpose settings, so if you want more control over the color and shapes, you can use the <tt>par.settings=list(superpose.symbol=)</tt> settings. Furthermore, you can use any of the lattice methods of adding a legend to your plot. The names used in the legend correspond to the names of the elements in the list you pass in.

Note that the confidence interval drawn depends on the total number of p-values given. When you pass in a list, the number of tests the confidence interval uses is determined by the vector with the '''least number of p-values''' - this gives the widest, most conservative confidence bands. Internally the different groups are drawn using the lattice superpose settings, so if you want more control over the color and shapes, you can use the <tt>par.settings=list(superpose.symbol=)</tt> settings. Furthermore, you can use any of the lattice methods of adding a legend to your plot. The names used in the legend correspond to the names of the elements in the list you pass in.