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<h2>DESCRIPTION</h2>
<em>r.quantile</em> computes quantiles in a manner suitable
for use with large amounts of data. It is using two passes.
<h2>NOTES</h2>
Quantiles are calculated following algorithm 7 from Hyndman and Fan (1996),
which is also the default in R and numpy.
<h2>EXAMPLE</h2>
Calculation of elevation quantiles (printed to standard-out):
<div class="code"><pre>
g.region raster=elevation -p
r.quantile input=elevation percentiles=0.1,1,10,25,50,75,90,99,99.9
</pre></div>
The output of <em>r.quantile</em> can be used for quantile classification:
<div class="code"><pre>
g.region raster=elevation -p
r.quantile elevation quantiles=5 -r --quiet | r.recode elevation \
out=elev_quant5 rules=-
</pre></div>
<h2>REFERENCES</h2>
<ul>
<li>Hyndman and Fan (1996) <i>Sample Quantiles in Statistical
Packages</i>, <b>American Statistician</b>. American Statistical
Association. 50 (4): 361-365. DOI: <a
href="https://doi.org/10.2307/2684934>10.2307/2684934">10.2307/2684934</a></li>
<li><a
href="https://www.itl.nist.gov/div898/handbook/prc/section2/prc262.htm"><i>Engineering
Statistics Handbook: Percentile</i></a>, NIST</li>
</ul>
<h2>SEE ALSO</h2>
<em>
<a href="r.mode.html">r.mode</a>,
<a href="r.quant.html">r.quant</a>,
<a href="r.recode.html">r.recode</a>,
<a href="r.series.html">r.series</a>,
<a href="r.stats.html">r.stats</a>,
<a href="r.stats.quantile.html">r.stats.quantile</a>,
<a href="r.stats.zonal.html">r.stats.zonal</a>,
<a href="r.statistics.html">r.statistics</a>,
<a href="r.univar.html">r.univar</a>,
<a href="v.rast.stats.html">v.rast.stats</a>
</em>
<h2>AUTHORS</h2>
Glynn Clements<br>
Markus Metz
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