===================================================================
RCS file: /home/cvs/OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd,v
retrieving revision 1.2
retrieving revision 1.3
diff -u -p -r1.2 -r1.3
--- OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd	2013/03/01 05:27:08	1.2
+++ OpenXM/src/R/r-packages/hgm/man/hgm.cwishart.Rd	2013/03/08 07:32:28	1.3
@@ -1,4 +1,4 @@
-% $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}
 \alias{hgm.cwishart}
 %- Also NEED an '\alias' for EACH other topic documented here.
@@ -11,14 +11,15 @@
   of random wishart matrix of size m times m.
 }
 \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.
 \arguments{
   \item{m}{The dimension of the Wishart matrix.}
   \item{n}{The degree of freedome (a parameter of the Wishart distribution)}
-  \item{beta}{The eigenvalues of the inverse of the covariant matrix
-(a parameter of the Wishart distribution)
+  \item{beta}{The eigenvalues of the inverse of the covariant matrix /2
+(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{approxdeg}{
@@ -33,19 +34,38 @@ hgm.cwishart(m,n,beta,x0,approxdeg,h,dp,x)
     Sampling interval of solutions by the Runge-Kutta method.
   }
   \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.
   }
+  \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{
   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.
-  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.
+  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}}.
 }
 \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
 function P(L1 < x) at x.  y[1],...,y[2^m] are some derivatives.
 See the reference below.
@@ -69,10 +89,15 @@ Nobuki Takayama
 }
 \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)
-
+## =====================================================
+## 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
 % R documentation directory.