===================================================================
RCS file: /home/cvs/OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v
retrieving revision 1.1
retrieving revision 1.4
diff -u -p -r1.1 -r1.4
--- OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd	2016/02/13 06:47:50	1.1
+++ OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd	2016/02/14 00:21:50	1.4
@@ -1,10 +1,10 @@
-% $OpenXM$
+% $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v 1.3 2016/02/13 22:56:50 takayama Exp $
 \name{hgm.p2wishart}
 \alias{hgm.p2wishart}
 %- Also NEED an '\alias' for EACH other topic documented here.
 \title{
     The function hgm.p2wishart evaluates the cumulative distribution function
-  of the largest eigenvalues of inverse(S2)*S1.
+  of the largest eigenvalues of W1*inverse(W2).
 }
 \description{
     The function hgm.p2wishart evaluates the cumulative distribution function
@@ -13,7 +13,7 @@
 }
 \usage{
 hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,method,
-            err,automatic,assigned_series_error,verbose)
+            err,automatic,assigned_series_error,verbose,autoplot)
 }
 %- maybe also 'usage' for other objects documented here.
 \arguments{
@@ -34,6 +34,7 @@ hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
   }
   \item{dp}{
     Sampling interval of solutions by the Runge-Kutta method.
+    When autoplot=1, dp is automatically set.
   }
   \item{q}{
     The second value y[0] of this function is the Prob(L1 < q)
@@ -41,9 +42,10 @@ hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
   }
   \item{mode}{
     When mode=c(1,0,0), it returns the evaluation 
-    of the matrix hypergeometric series and its derivatives at x0.
+    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
     p-steps of x are also returned.  Sampling interval is controled by dp.
+    When autoplot=1, mode is automatically set.
   }
   \item{method}{
     a-rk4 is the default value. 
@@ -72,14 +74,19 @@ hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
     If it is 1, then steps of automatic degree updates and several parameters
     are output to stdout and stderr.
   }  
+  \item{autoplot}{
+    autoplot=0 is the default value.
+    If it is 1, then it outputs an input for plot.
+    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,...), ...
+  }  
 }
 \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 is smaller,
-  x0 is relevant value (not very big, not very small),
+  q0 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
+  A heuristic method to set parameters q0, h, approxdeg properly
   is to make x larger and to check if the y[0] approaches to 1.
 %  \code{\link[RCurl]{postForm}}.
 }
@@ -119,8 +126,13 @@ hgm.p2wishart(m=3,n1=5,n2=10,beta=c(1,2,4),q=4)
 ## =====================================================
 ## Example 2. 
 ## =====================================================
-b<-hgm.p2wishart(m=3,n1=5,n2=10,beta=c(1,2,4),q0=0.1,q=20,approxdeg=20,mode=c(1,1,(8+1)*100));
-c<-matrix(b,ncol=16+1,byrow=1);
+b<-hgm.p2wishart(m=3,n1=5,n2=10,beta=c(1,2,4),q0=0.3,q=20,approxdeg=20,mode=c(1,1,(8+1)*1000));
+c<-matrix(b,ncol=8+1,byrow=1);
+#plot(c)
+## =====================================================
+## Example 3. 
+## =====================================================
+c<-hgm.p2wishart(m=3,n1=5,n2=10,beta=c(1,2,4),q0=0.3,q=20,approxdeg=20,autoplot=1);
 #plot(c)
 }
 % Add one or more standard keywords, see file 'KEYWORDS' in the