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Diff for /OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd between version 1.3 and 1.8

version 1.3, 2016/02/13 22:56:50 version 1.8, 2016/10/28 02:27:39
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 % $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v 1.2 2016/02/13 07:12:52 takayama Exp $  % $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v 1.7 2016/03/01 07:29:18 takayama Exp $
 \name{hgm.p2wishart}  \name{hgm.p2wishart}
 \alias{hgm.p2wishart}  \alias{hgm.p2wishart}
 %- Also NEED an '\alias' for EACH other topic documented here.  %- Also NEED an '\alias' for EACH other topic documented here.
Line 34  hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
Line 34  hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
   }    }
   \item{dp}{    \item{dp}{
     Sampling interval of solutions by the Runge-Kutta method.      Sampling interval of solutions by the Runge-Kutta method.
     When autoplot=1, dp is automatically set.      When autoplot=1 or dp is negative, it is automatically set.
       if it is 0, no sample is stored.
   }    }
   \item{q}{    \item{q}{
     The second value y[0] of this function is the Prob(L1 < q)      The second value y[0] of this function is the Prob(L1 < q)
Line 43  hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
Line 44  hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
   \item{mode}{    \item{mode}{
     When mode=c(1,0,0), it returns the evaluation      When mode=c(1,0,0), it returns the evaluation
     of the matrix hypergeometric series and its derivatives at q0.      of the matrix hypergeometric series and its derivatives at q0.
     When mode=c(1,1,(m^2+1)*p), intermediate values of P(L1 < x) with respect to      When mode=c(1,1,(2^m+1)*p), intermediate values of P(L1 < x) with respect to
     p-steps of x are also returned.  Sampling interval is controled by dp.      p-steps of x are also returned.  Sampling interval is controled by dp.
     When autoplot=1, mode is automatically set.      When autoplot=1, mode is automatically set.
   }    }
Line 54  hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
Line 55  hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
   }    }
   \item{err}{    \item{err}{
     When err=c(e1,e2), e1 is the absolute error and e2 is the relative error.      When err=c(e1,e2), e1 is the absolute error and e2 is the relative error.
     As long as NaN is not returned, it is recommended to set to      This parameter controls the adative Runge-Kutta method.
     err=c(0.0, 1e-10), because initial values are usually very small.      If the output is absurd, you may get a correct answer by setting,  e.g.,
       err=c(1e-(xy+5), 1e-10) or by increasing q0 when initial value at q0 is very small as 1e-xy.
   }    }
   \item{automatic}{    \item{automatic}{
     automatic=1 is the default value.      automatic=1 is the default value.
Line 76  hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
Line 78  hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
   }    }
   \item{autoplot}{    \item{autoplot}{
     autoplot=0 is the default value.      autoplot=0 is the default value.
     If it is 1, then it outputs an input for plot.      If it is 1, then this function outputs an input for plot
       (which is equivalent to setting the 3rd argument of the mode parameter properly).
       When ans is the output, ans[1,] is c(q,prob at q,...), ans[2,] is c(q0,prob at q0,...), and ans[3,] is c(q0+q/100,prob at q/100,...), ...
       When the adaptive Runge-Kutta method is used, the step size h may change
       automatically,
       which  makes the sampling period change, in other words, the sampling points
      q0+q/100, q0+2*q/100, q0+3*q/100, ... may  change.
      In this case, the output matrix may contain zero rows in the tail or overfull.
      In case of the overful, use the mode option to get the all result.
   }    }
 }  }
 \details{  \details{
Line 97  See the reference below.
Line 107  See the reference below.
 }  }
 \references{  \references{
 H.Hashiguchi, N.Takayama, A.Takemura,  H.Hashiguchi, N.Takayama, A.Takemura,
 in preparation.  Distribution of ratio of two Wishart matrices and evaluation of cumulative probability by holonomic gradient method.
 }  }
 \author{  \author{
 Nobuki Takayama  Nobuki Takayama
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