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  <refnamediv>
    <refname>distfun_kspdf</refname>
    <refpurpose>Kolmogorov-Smirnov PDF</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Calling Sequence</title>
   <synopsis>
   y = distfun_kspdf(x,N)
   
   </synopsis>
</refsynopsisdiv>

<refsection>
   <title>Parameters</title>
   <variablelist>
   <varlistentry><term>x :</term>
      <listitem><para> a matrix of doubles, the outcome. x in [0,1]</para></listitem></varlistentry>
   <varlistentry><term>N :</term>
      <listitem><para> a matrix of doubles , the number of observations. N belongs to the set {1,2,3,4,.......,2147483647}</para></listitem></varlistentry>
   <varlistentry><term>y :</term>
      <listitem><para> a matrix of doubles, the probability density.</para></listitem></varlistentry>
   </variablelist>
</refsection>

<refsection>
   <title>Description</title>
   <para>
Computes the probability distribution function of
the Kolmogorov-Smirnov distribution function.
   </para>
   <para>
Any scalar input argument is expanded to a matrix of doubles
of the same size as the other input arguments.
   </para>
   <para>
</para>
</refsection>

<refsection>
   <title>Examples</title>
   <programlisting role="example"><![CDATA[
// Check with x scalar, N scalar, pr scalar
y = distfun_kspdf(0.274,10)
expected = 0.0175879

// Plot the function
scf();
x = linspace(0,1);
y1 = distfun_kspdf(x,5);
plot(x,y1,"b-")
y2 = distfun_kspdf(x,10);
plot(x,y2,"g-")
y3 = distfun_kspdf(x,20);
plot(x,y3,"r-")
legend(["N=5","N=10","N=20"]);
xtitle("Kolmogorov-Smirnov PDF","x","P(X=x)")

   ]]></programlisting>
</refsection>

<refsection>
   <title>Bibliography</title>
   <para>http://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test</para>
</refsection>

<refsection>
   <title>Authors</title>
   <simplelist type="vert">
   <member>Copyright (C) 2014 - Michael Baudin</member>
   </simplelist>
</refsection>
</refentry>
