version 1.1, 2013/02/23 07:00:21 |
version 1.4, 2013/03/26 05:53:57 |
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% $OpenXM$ |
% $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd,v 1.3 2013/03/08 07:32:28 takayama Exp $ |
\name{hgm.cwishart} |
\name{hgm.pwishart} |
\alias{hgm.cwishart} |
\alias{hgm.pwishart} |
%- Also NEED an '\alias' for EACH other topic documented here. |
%- Also NEED an '\alias' for EACH other topic documented here. |
\title{ |
\title{ |
The function hgm.cwishart evaluates the cumulative distribution function |
The function hgm.pwishart evaluates the cumulative distribution function |
of random wishart matrix. |
of random wishart matrix. |
} |
} |
\description{ |
\description{ |
The function hgm.cwishart evaluates the cumulative distribution function |
The function hgm.pwishart evaluates the cumulative distribution function |
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.pwishart(m,n,beta,q0,approxdeg,h,dp,q,mode,method,err) |
} |
} |
%- maybe also 'usage' for other objects documented here. |
%- maybe also 'usage' for other objects documented here. |
\arguments{ |
\arguments{ |
\item{m}{} |
\item{m}{The dimension of the Wishart matrix.} |
\item{n}{} |
\item{n}{The degree of freedome (a parameter of the Wishart distribution)} |
\item{beta}{ |
\item{beta}{The eigenvalues of the inverse of the covariant matrix /2 |
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(a parameter of the Wishart distribution). |
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The beta is equal to inverse(sigma)/2. |
} |
} |
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\item{q0}{The point to evaluate the matrix hypergeometric series. q0>0} |
\item{approxdeg}{ |
\item{approxdeg}{ |
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Zonal polynomials upto the approxdeg are calculated to evaluate |
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values near the origin. A zonal polynomial is determined by a given |
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partition (k1,...,km). We call the sum k1+...+km the degree. |
} |
} |
\item{h}{ |
\item{h}{ |
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A (small) step size for the Runge-Kutta method. h>0. |
} |
} |
\item{dp}{ |
\item{dp}{ |
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Sampling interval of solutions by the Runge-Kutta method. |
} |
} |
\item{x}{} |
\item{q}{ |
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The second value y[0] of this function is the Prob(L1 < q) |
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where L1 is the first eigenvalue of the Wishart matrix. |
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} |
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\item{mode}{ |
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When mode=c(1,0,0), it returns the evaluation |
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of the matrix hypergeometric series and its derivatives at x0. |
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When mode=c(1,1,(m^2+1)*p), intermediate values of P(L1 < x) with respect to |
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p-steps of x are also returned. Sampling interval is controled by dp. |
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} |
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\item{method}{ |
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rk4 is the default value. |
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When method="a-rk4", the adaptive Runge-Kutta method is used. |
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Steps are automatically adjusted by err. |
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} |
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\item{err}{ |
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When err=c(e1,e2), e1 is the absolute error and e2 is the relative error. |
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As long as NaN is not returned, it is recommended to set to |
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err=c(0.0, 1e-10), because initial values are usually very small. |
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} |
} |
} |
\details{ |
\details{ |
It is evaluated by the Koev-Edelman algorithm near the origin and |
It is evaluated by the Koev-Edelman algorithm when x is near the origin and |
and by the HGM when x is far from the origin. |
by the HGM when x is far from the origin. |
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We can obtain more accurate result when the variables h is smaller, |
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x0 is relevant value (not very big, not very small), |
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and the approxdeg is more larger. |
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A heuristic method to set parameters x0, h, approxdeg properly |
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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[0] is the value of the cumulative distribution |
The output is x, y[0], ..., y[2^m] in the default mode, |
function at x. y[1],... are some derivatives. |
y[0] is the value of the cumulative distribution |
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function P(L1 < x) at x. y[1],...,y[2^m] are some derivatives. |
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See the reference below. |
} |
} |
\references{ |
\references{ |
HNTT, |
H.Hashiguchi, Y.Numata, N.Takayama, A.Takemura, |
\url{http://arxiv.org/abs/??}, |
Holonomic gradient method for the distribution function of the largest root of a Wishart matrix |
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\url{http://arxiv.org/abs/1201.0472}, |
} |
} |
\author{ |
\author{ |
Nobuki Takayama |
Nobuki Takayama |
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} |
} |
\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.pwishart(m=3,n=5,beta=c(1,2,3),q=10) |
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## ===================================================== |
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## Example 2. |
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## ===================================================== |
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b<-hgm.pwishart(m=4,n=10,beta=c(1,2,3,4),q0=1,q=10,approxdeg=20,mode=c(1,1,(16+1)*100)); |
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c<-matrix(b,ncol=16+1,byrow=1); |
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#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. |