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### for working under R < 2.3.0
if(getRversion()<'2.3.0')
{ ## ignore ncp
###beta Distribution
qbeta <- function(p, shape1, shape2, ncp = 0, lower.tail = TRUE,
log.p = FALSE)
{if(isTRUE(all.equal(ncp,0)))
stats::qbeta(p, shape1, shape2, lower.tail, log.p)
else
{x <- c(0.0,1.0)
pfun <- function(x)
{ pbeta(x, shape1=shape1, shape2=shape2, ncp=ncp)}
qfun <- P2Q(pfun,x)
p <- ifelse(log.p,exp(p),p)
p <- ifelse(lower.tail,p,1-p)
qfun(p)
}
}
rbeta <- function(n, shape1, shape2, ncp = 0)
{if(isTRUE(all.equal(ncp,0)))
stats::rbeta(n, shape1, shape2)
else
{X <- rchisq(n,df=2*shape1,ncp=ncp)
Y <- rchisq(n,df=2*shape2,ncp=0)
X/(X+Y)}
}
###F Distribution
qf <- function(p, df1, df2, ncp = 0, lower.tail = TRUE, log.p = FALSE)
{if(isTRUE(all.equal(ncp,0)))
stats::qf(p, df1, df2, lower.tail, log.p)
else
{TQ <- getdistrOption("TruncQuantile")
xz <- qchisq(1-TQ,df=df1,ncp=ncp); xn<-qchisq(TQ,df=df2,ncp=0)
x <- c(0,xz/xn*df2/df1)
pfun <- function(x){pf(x, df1=df1, df2=df2, ncp=ncp)}
qfun <- P2Q(pfun,x)
p <- ifelse(log.p,exp(p),p)
p <- ifelse(lower.tail,p,1-p)
qfun(p)
}
}
rf <- function(n, df1, df2, ncp = 0)
{if(isTRUE(all.equal(ncp,0)))
stats::rf(n, df1, df2)
else df2*rchisq(n, df=df1, ncp=ncp)/rchisq(n, df=df2, ncp=0)/df1
}
###T Distribution
qt <- function(p, df, ncp = 0, lower.tail = TRUE, log.p = FALSE)
{if(isTRUE(all.equal(ncp,0)))
stats::qt(p, df, lower.tail, log.p)
else
{TQ <- getdistrOption("TruncQuantile")*2
xz <- qnorm(1-TQ,mean=df); xn<-sqrt(qchisq(TQ,df=df,ncp=0)/df)
x <- c(-xz/xn,xz/xn)
pfun <- function(x){pt(x, df=df, ncp=ncp)}
qfun <- P2Q(pfun,x)
p <- ifelse(log.p,exp(p),p)
p <- ifelse(lower.tail,p,1-p)
qfun(p)
}
}
rt <- function(n, df, ncp = 0)
{if(isTRUE(all.equal(ncp,0)))
stats::rt(n, df)
else rnorm(n,mean=ncp)/sqrt(rchisq(n,df=df)/df)
}
}
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