===================================================================
RCS file: /home/cvs/OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v
retrieving revision 1.2
retrieving revision 1.5
diff -u -p -r1.2 -r1.5
--- OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd	2016/02/13 07:12:52	1.2
+++ OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd	2016/02/15 07:42:07	1.5
@@ -1,10 +1,10 @@
-% $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v 1.1 2016/02/13 06:47:50 takayama Exp $
+% $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v 1.4 2016/02/14 00:21:50 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
@@ -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.
-    When mode=c(1,1,(m^2+1)*p), intermediate values of P(L1 < x) with respect to
+    of the matrix hypergeometric series and its derivatives at q0.
+    When mode=c(1,1,(2^m+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. 
@@ -52,8 +54,9 @@ hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
   }
   \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. 
+    This parameter controls the adative Runge-Kutta method.
+    If the output is absurd, you may get a correct answer by setting,  e.g.,
+    err=c(1e-(xy+5), 1e-10) when initial value at q0 is very small as 1e-xy. 
   }  
   \item{automatic}{
     automatic=1 is the default value.
@@ -75,15 +78,16 @@ hgm.p2wishart(m,n1,n2,beta,q0,approxdeg,h,dp,q,mode,me
   \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}}.
 }