version 1.4, 2013/03/26 05:53:57 |
version 1.6, 2014/03/24 05:28:17 |
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% $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd,v 1.3 2013/03/08 07:32:28 takayama Exp $ |
% $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd,v 1.5 2014/03/16 03:11:07 takayama Exp $ |
\name{hgm.pwishart} |
\name{hgm.pwishart} |
\alias{hgm.pwishart} |
\alias{hgm.pwishart} |
%- Also NEED an '\alias' for EACH other topic documented here. |
%- Also NEED an '\alias' for EACH other topic documented here. |
Line 44 hgm.pwishart(m,n,beta,q0,approxdeg,h,dp,q,mode,method, |
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Line 44 hgm.pwishart(m,n,beta,q0,approxdeg,h,dp,q,mode,method, |
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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. |
} |
} |
\item{method}{ |
\item{method}{ |
rk4 is the default value. |
a-rk4 is the default value. |
When method="a-rk4", the adaptive Runge-Kutta method is used. |
When method="a-rk4", the adaptive Runge-Kutta method is used. |
Steps are automatically adjusted by err. |
Steps are automatically adjusted by err. |
} |
} |
Line 53 hgm.pwishart(m,n,beta,q0,approxdeg,h,dp,q,mode,method, |
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Line 53 hgm.pwishart(m,n,beta,q0,approxdeg,h,dp,q,mode,method, |
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As long as NaN is not returned, it is recommended to set to |
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. |
err=c(0.0, 1e-10), because initial values are usually very small. |
} |
} |
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\item{automatic}{ |
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automatic=1 is the default value. |
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If it is 1, the degree of the series approximation will be increased until |
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|(F(i)-F(i-1))/F(i-1)| < assigned_series_error where |
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F(i) is the degree i approximation of the hypergeometric series |
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with matrix argument. |
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Step sizes for the Runge-Kutta method are also set automatically from |
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the assigned_series_error if it is 1. |
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} |
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\item{assigned_series_error}{ |
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assigned_series_error=0.00001 is the default value. |
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} |
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\item{verbose}{ |
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verbose=0 is the default value. |
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If it is 1, then steps of automatic degree updates and several parameters |
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are output to stdout and stderr. |
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} |
} |
} |
\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 |
Line 72 See the reference below. |
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Line 89 See the reference below. |
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} |
} |
\references{ |
\references{ |
H.Hashiguchi, Y.Numata, N.Takayama, A.Takemura, |
H.Hashiguchi, Y.Numata, N.Takayama, A.Takemura, |
Holonomic gradient method for the distribution function of the largest root of a Wishart matrix |
Holonomic gradient method for the distribution function of the largest root of a Wishart matrix, |
\url{http://arxiv.org/abs/1201.0472}, |
Journal of Multivariate Analysis, 117, (2013) 296-312, |
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\url{http://dx.doi.org/10.1016/j.jmva.2013.03.011}, |
} |
} |
\author{ |
\author{ |
Nobuki Takayama |
Nobuki Takayama |