=================================================================== RCS file: /home/cvs/OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v retrieving revision 1.1 retrieving revision 1.7 diff -u -p -r1.1 -r1.7 --- 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 2016/03/01 07:29:18 1.7 @@ -1,10 +1,10 @@ -% $OpenXM$ +% $OpenXM: OpenXM/src/R/r-packages/hgm/man/hgm.c2wishart.Rd,v 1.6 2016/02/16 02:17:00 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 +34,8 @@ 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 or dp is negative, it is automatically set. + if it is 0, no sample is stored. } \item{q}{ The second value y[0] of this function is the Prob(L1 < q) @@ -41,9 +43,10 @@ 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 +55,9 @@ 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. @@ -72,14 +76,26 @@ 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 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}}. } @@ -119,8 +135,13 @@ 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