[BACK]Return to hgm.cwishart.Rd CVS log [TXT][DIR] Up to [local] / OpenXM / src / R / r-packages / hgm / man

Diff for /OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd between version 1.2 and 1.3

version 1.2, 2013/03/01 05:27:08 version 1.3, 2013/03/08 07:32:28
Line 1 
Line 1 
 % $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd,v 1.1 2013/02/23 07:00:21 takayama Exp $  % $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd,v 1.2 2013/03/01 05:27:08 takayama Exp $
 \name{hgm.cwishart}  \name{hgm.cwishart}
 \alias{hgm.cwishart}  \alias{hgm.cwishart}
 %- Also NEED an '\alias' for EACH other topic documented here.  %- Also NEED an '\alias' for EACH other topic documented here.
Line 11 
Line 11 
   of random wishart matrix of size m times m.    of random wishart matrix of size m times m.
 }  }
 \usage{  \usage{
 hgm.cwishart(m,n,beta,x0,approxdeg,h,dp,x)  hgm.cwishart(m,n,beta,x0,approxdeg,h,dp,x,mode,method,err)
 }  }
 %- maybe also 'usage' for other objects documented here.  %- maybe also 'usage' for other objects documented here.
 \arguments{  \arguments{
   \item{m}{The dimension of the Wishart matrix.}    \item{m}{The dimension of the Wishart matrix.}
   \item{n}{The degree of freedome (a parameter of the Wishart distribution)}    \item{n}{The degree of freedome (a parameter of the Wishart distribution)}
   \item{beta}{The eigenvalues of the inverse of the covariant matrix    \item{beta}{The eigenvalues of the inverse of the covariant matrix /2
 (a parameter of the Wishart distribution)  (a parameter of the Wishart distribution).
       The beta is equal to inverse(sigma)/2.
   }    }
   \item{x0}{The point to evaluate the matrix hypergeometric series. x0>0}    \item{x0}{The point to evaluate the matrix hypergeometric series. x0>0}
   \item{approxdeg}{    \item{approxdeg}{
Line 33  hgm.cwishart(m,n,beta,x0,approxdeg,h,dp,x)
Line 34  hgm.cwishart(m,n,beta,x0,approxdeg,h,dp,x)
     Sampling interval of solutions by the Runge-Kutta method.      Sampling interval of solutions by the Runge-Kutta method.
   }    }
   \item{x}{    \item{x}{
     The first value of this function is the Prob(L1 < x)      The second value y[0] of this function is the Prob(L1 < x)
     where L1 is the first eigenvalue of the Wishart matrix.      where L1 is the first eigenvalue of the Wishart matrix.
   }    }
     \item{mode}{
       When mode=c(1,0,0), it returns the evaluation
       of the matrix hypergeometric series and its derivatives at x0.
       When mode=c(1,1,(m^2+1)*p), intermediate values of P(L1 < x) with respect to
       p-steps of x are also returned.  Sampling interval is controled by dp.
     }
     \item{method}{
       rk4 is the default value.
       When method="a-rk4", the adaptive Runge-Kutta method is used.
       Steps are automatically adjusted by err.
     }
     \item{err}{
       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
       err=c(0.0, 1e-10), because initial values are usually very small.
     }
 }  }
 \details{  \details{
   It is evaluated by the Koev-Edelman algorithm when x is near the origin and    It is evaluated by the Koev-Edelman algorithm when x is near the origin and
   by the HGM when x is far from the origin.    by the HGM when x is far from the origin.
   We can obtain more accurate result when the variables h, x0 are smaller    We can obtain more accurate result when the variables h is smaller,
     x0 is relevant value (not very big, not very small),
   and the approxdeg is more larger.    and the approxdeg is more larger.
     A heuristic method to set parameters x0, h, approxdeg properly
     is to make x larger and to check if the y[0] approaches to 1.
 %  \code{\link[RCurl]{postForm}}.  %  \code{\link[RCurl]{postForm}}.
 }  }
 \value{  \value{
 The output is x, y[0], ..., y[2^m],  The output is x, y[0], ..., y[2^m] in the default mode,
 y[0] is the value of the cumulative distribution  y[0] is the value of the cumulative distribution
 function P(L1 < x) at x.  y[1],...,y[2^m] are some derivatives.  function P(L1 < x) at x.  y[1],...,y[2^m] are some derivatives.
 See the reference below.  See the reference below.
Line 69  Nobuki Takayama
Line 89  Nobuki Takayama
 }  }
 \examples{  \examples{
 ## =====================================================  ## =====================================================
 ## Example 1. Computing normalization constant of the Fisher distribution on SO(3)  ## Example 1.
 ## =====================================================  ## =====================================================
 hgm.cwishart(m=3,n=5,beta=c(1,2,3),x=10)  hgm.cwishart(m=3,n=5,beta=c(1,2,3),x=10)
   ## =====================================================
   ## Example 2.
   ## =====================================================
   b<-hgm.cwishart(m=4,n=10,beta=c(1,2,3,4),x0=1,x=10,approxdeg=20,mode=c(1,1,(16+1)*100));
   c<-matrix(b,ncol=16+1,byrow=1);
   #plot(c)
 }  }
 % Add one or more standard keywords, see file 'KEYWORDS' in the  % Add one or more standard keywords, see file 'KEYWORDS' in the
 % R documentation directory.  % R documentation directory.
Legend:
Removed from v.1.2  
changed lines
  Added in v.1.3
FreeBSD-CVSweb <freebsd-cvsweb@FreeBSD.org>