<?xml version="1.0" encoding="UTF-8"?>

<!--
 *
 * This help file was generated from distfun_chi2cdf.sci using help_from_sci().
 *
 -->

<refentry version="5.0-subset Scilab" xml:id="distfun_chi2cdf" xml:lang="en"
          xmlns="http://docbook.org/ns/docbook"
          xmlns:xlink="http://www.w3.org/1999/xlink"
          xmlns:svg="http://www.w3.org/2000/svg"
          xmlns:ns3="http://www.w3.org/1999/xhtml"
          xmlns:mml="http://www.w3.org/1998/Math/MathML"
          xmlns:scilab="http://www.scilab.org"
          xmlns:db="http://docbook.org/ns/docbook">

  <refnamediv>
    <refname>distfun_chi2cdf</refname>
    <refpurpose>Chi-squared CDF</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Calling Sequence</title>
   <synopsis>
   p = distfun_chi2cdf(x,k)
   p = distfun_chi2cdf(x,k,lowertail)
   
   </synopsis>
</refsynopsisdiv>

<refsection>
   <title>Parameters</title>
   <variablelist>
   <varlistentry><term>x :</term>
      <listitem><para> a matrix of doubles, the outcome, greater or equal to zero</para></listitem></varlistentry>
   <varlistentry><term>k :</term>
      <listitem><para> a matrix of doubles, the number of degrees of freedom, k>0 (can be non integer)</para></listitem></varlistentry>
   <varlistentry><term>lowertail :</term>
      <listitem><para> a 1-by-1 matrix of booleans, the tail (default lowertail=%t). If lowertail is true (the default), then considers P(X&lt;=x) otherwise P(X&gt;x).</para></listitem></varlistentry>
   <varlistentry><term>p :</term>
      <listitem><para> a matrix of doubles, the probability.</para></listitem></varlistentry>
   </variablelist>
</refsection>

<refsection>
   <title>Description</title>
   <para>
Computes the cumulative distribution function of
the Chi-squared 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[
// Test with x scalar, k scalar
computed = distfun_chi2cdf(4,5)
expected = 0.4505840

// Test with expanded x, k scalar
computed = distfun_chi2cdf([2 6],5)
expected = [0.1508550 0.6937811]

// Test with x scalar, k expanded
computed = distfun_chi2cdf(4,[4 7])
expected = [0.5939942 0.2202226]

// Test with both x,k expanded
computed = distfun_chi2cdf([2 6],[3 4])
expected = [0.4275933 0.8008517]

// Plot the function
h=scf();
k = [2 3 4 6 9 12];
cols = [1 2 3 4 5 6];
lgd = [];
for i = 1:size(k,'c')
x = linspace(0,10,1000);
y = distfun_chi2cdf ( x , k(i) );
plot(x,y)
str = msprintf("k=%s",string(k(i)));
lgd($+1) = str;
end
for i = 1:size(k,'c')
hcc = h.children.children;
hcc.children(size(k,'c') - i + 1).foreground = cols(i);
end
xtitle("Chi-squared CDF","x","$P(X\leq x)$");
legend(lgd);

   ]]></programlisting>
</refsection>

<refsection>
   <title>Bibliography</title>
   <para>http://en.wikipedia.org/wiki/Chi-squared_distribution</para>
</refsection>

<refsection>
   <title>Authors</title>
   <simplelist type="vert">
   <member>Copyright (C) 2012 - Prateek Papriwal</member>
   <member>Copyright (C) 2012 - Michael Baudin</member>
   </simplelist>
</refsection>
</refentry>
