=================================================================== RCS file: /home/cvs/OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v retrieving revision 1.2 retrieving revision 1.6 diff -u -p -r1.2 -r1.6 --- 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 2016/02/16 02:17:00 1.6 @@ -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 1.5 2016/02/15 07:42:07 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) or by increasing q0 when initial value at q0 is very small as 1e-xy. } \item{automatic}{ automatic=1 is the default value. @@ -74,16 +77,24 @@ 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. + If it is 1, then this function outputs an input for plot + (which is equivalent to setting the 3rd argument of the mode parameter properly). + 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,...), ... + When the adaptive Runge-Kutta method is used, the step size h may change + automatically, + which makes the sampling period change, in other words, the sampling points + q0+q/100, q0+2*q/100, q0+3*q/100, ... may change. + In this case, the output matrix may contain zero rows in the tail or overfull. + In case of the overful, use the mode option to get the all result. } } \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}}. }