qbetaAppr: Compute (Approximate) Quantiles of the Beta Distribution
In DPQ: Density, Probability, Quantile ('DPQ') Computations

qbetaAppr

R Documentation

Compute (Approximate) Quantiles of the Beta Distribution

Description

Compute quantiles (inverse distribution values) of the beta distribution, using diverse approximations.

Usage


qbetaAppr.1(a, p, q, lower.tail=TRUE, log.p=FALSE,
            y = qnormUappr(a, lower.tail=lower.tail, log.p=log.p))

qbetaAppr.2(a, p, q, lower.tail=TRUE, log.p=FALSE, logbeta = lbeta(p,q))
qbetaAppr.3(a, p, q, lower.tail=TRUE, log.p=FALSE, logbeta = lbeta(p,q))
qbetaAppr.4(a, p, q, lower.tail=TRUE, log.p=FALSE,
            y = qnormUappr(a, lower.tail=lower.tail, log.p=log.p),
            verbose = getOption("verbose"))

qbetaAppr  (a, p, q, lower.tail=TRUE, log.p=FALSE,
            y = qnormUappr(a, lower.tail=lower.tail, log.p=log.p),
            logbeta = lbeta(p,q),
            verbose = getOption("verbose") && length(a) == 1)

qbeta.R    (alpha, p, q,
            lower.tail = TRUE, log.p = FALSE,
	    logbeta = lbeta(p,q),
	    low.bnd = 3e-308, up.bnd = 1-2.22e-16,
            method = c("AS109", "Newton-log"),
            tol.outer = 1e-15,
	    f.acu = function(a,p,q) max(1e-300, 10^(-13- 2.5/pp^2 - .5/a^2)),
	    fpu = .Machine$ double.xmin,
	    qnormU.fun = function(u, lu) qnormUappr(p=u, lp=lu)
          , R.pre.2014 = FALSE
	  , verbose = getOption("verbose")
          , non.finite.report = verbose
           )

Arguments

`a`, `alpha`	vector of probabilities (otherwise, e.g., in `qbeta()`, called `p`).
`p`, `q`	the two shape parameters of the beta distribution; otherwise, e.g., in `qbeta()`, called `shape1` and `shape2`.
`y`	an approximation to `\Phi^{-1}(1-\alpha)` (aka `z_{1-\alpha}`) where `\Phi(x)` is the standard normal cumulative probability function and `\Phi{-1}(x)` its inverse, i.e., R's `qnorm(x)`.
`lower.tail`, `log.p`	logical, see, e.g., `qchisq()`; must have length 1.
`logbeta`	must be `lbeta(p,q)`; mainly an option to pass a value already computed.
`verbose`	logical or integer indicating if and how much “monitoring” information should be produced by the algorithm.
`low.bnd`, `up.bnd`	lower and upper bounds for ...TODO...
`method`	a string specifying the approximation method to be used.
`tol.outer`	the “outer loop” convergence tolerance; the default `1e-15` has been hardwired in R's `qbeta()`.
`f.acu`	a `function` with arguments `(a,p,q)` ...TODO...
`fpu`	a very small positive number.
`qnormU.fun`	a `function` with arguments `(u,lu)` to compute “the same” as `qnormUappr()`, the upper standard normal quantile.
`R.pre.2014`	a `logical` ... TODO ...
`non.finite.report`	`logical` indicating if during the “outer loop” refining iterations, if `y` becomes non finite and the iterations have to stop, it should be reported (before the current best value is returned).

Value

...

Author(s)

The R Core Team for the C version of qbeta in R's sources; Martin Maechler for the R port, and the approximations.

Examples

 qbeta.R(0.6, 2, 3) # 0.4445
 qbeta.R(0.6, 2, 3) - qbeta(0.6, 2,3) # almost 0

 qbetaRV <- Vectorize(qbeta.R, "alpha") # now can use
 curve(qbetaRV(x, 1.5, 2.5))
 curve(qbeta  (x, 1.5, 2.5), add=TRUE, lwd = 3, col = adjustcolor("red", 1/2))

 ## an example of disagreement (and doubt, as borderline, close to underflow):
 qbeta.R(0.5078, .01, 5) # ->  2.77558e-15    # but
 qbeta  (0.5078, .01, 5) # now gives 4.651188e-31  -- correctly!
 qbeta  (0.5078, .01, 5, ncp=0)# ditto
 ## which is because qbeta() now works in log-x scale here:
 curve(pbeta(x, .01, 5), 1e-40, 1, n=10001, log="x", xaxt="n")
 sfsmisc::eaxis(1); abline(h=.5078, lty=3); abline(v=4.651188e-31,col=2)

DPQ documentation built on July 18, 2025, 3:01 p.m.