title: R: Distribution Function returns "random" values
body: <p>I estimated a 5 dimensional normal copula and call the pCopula function with arbitrary upper bounds. As you can see the results vary when the same  function is called several times: </p>
  
  <pre><code> library(copula)
  normal.cop = normalCopula( c(0.5517099 ,0.3519115, 0.5681927, 0.4931297, 0.3733265, 0.4956172, 0.3931483, 0.4177506, 0.4112307, 0.6423421), dim=5, dispstr = "un") 
  
  pCopula(c(0.4,0.5,0.3,0.2,0.9),normal.cop)
  &gt;0.07235714
  pCopula(c(0.4,0.5,0.3,0.2,0.9),normal.cop)
  &gt;0.07233399
  </code></pre>
  
  <p>The function is obviously using some Monte Carlo integration methods because for identical set.seed values the results are the same: </p>
  
  <pre><code>set.seed(1)
  pCopula(c(0.4,0.5,0.3,0.2,0.9),normal.cop)
  &gt;0.07234068
  </code></pre>
  
  <p>Where can I change the default settings of the function? I needs very reliable results and would like to set the number of iterations, error tolerance etc. manually. The function does not have any parameters to do that.</p>
  
  <p>According to the <a href="http://127.0.0.1:30167/library/copula/html/Copula.html" rel="nofollow">documentation</a> however the functions uses pmvnorm. There is no random component in pmvnorm and I can set iterations in pmvnorm. Is there any option to do that in pCoupla? </p>